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Identifying frequent item-sets is a popular data-mining task. It consists of finding sets of items frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets may be time-consuming, and a user may be interested merely in some specific item-sets rather than all of them. Recently, methods have been proposed for targeted item-set mining; that is to calculate the support of some item-sets of interest. Though this approach is often more suitable for real applications than traditional item-set mining approaches, performance remains an issue. To address that issue, this paper presents a novel algorithm for multitude-targeted mining, named Guided Frequent Pattern-Growth (GFP-Growth). The GFP-Growth algorithm is designed to quickly mine a given set of item-sets using a small amount of memory. This paper proves that GFP-Growth yields the exact frequency-counts for each item-set of interest. It further shows that GFP-Growth can boost the performance for several problems requiring item-set mining. We specifically study the problem of generating minority-class rules from imbalanced data and develop the Minority-Report Algorithm (MRA) that uses GFP-Growth to solve this problem efficiently. We prove several theoretical properties of MRA and present experimental results showing substantial performance gain. [ABSTRACT FROM AUTHOR]

This paper proposes a high-performance bidirectional architecture for the quasi-comparison-free sorting algorithm. Our architecture improves the performance of the conventional unidirectional architecture by reducing the total number of sorting cycles via bidirectional sorting along with two auxiliary methods. Bidirectional sorting allows the sorting tasks to be conducted concurrently in the high- and low-index parts of our architecture. The first auxiliary method is boundary finding, which shortens the range for index searching by finding the boundaries of the range. The second auxiliary method is queue storing, which stores each useful index in a queue in advance to reduce the number of miss cycles during index searching. The performance of our architecture highly depends on the distribution of input data. For each set of input data to be sorted, five Gaussian distributions of the input data and four standard derivations for each distribution were adopted in our experiments. The results show that at the expense of some additional area cost, the number of sorting cycles and the energy consumption are significantly reduced by our method. [ABSTRACT FROM AUTHOR]

• The paper provides two co-clustering algorithms for distributional-valued data. • The paper uses L2 Wasserstein distances to compare distributional-valued data. • The algorithms compute the weights of relevance of the distributional variables. • The algorithms compute the weights of relevance of the variable's components. • Applications with synthetic and real data sets corroborate the proposed method. This paper is concerned with the co-clustering of distribution-valued data, that is, the simultaneous partitioning of rows and columns of an input data table, the elements of which are distributions (or histograms) representing aggregate data. The first proposed method extends the double k-means algorithm to distributional data. The L 2 Wasserstein distance, also known as Mallow's distance, is used to compare distributions. To consider the different relevance of the variables characterizing the clusters, four variants of adaptive distributional double k-means are proposed. Accordingly, in the co-clustering procedure, an additional step is introduced to compute the relevance weights associated with the variables. In particular, each of the four algorithms provides i) a set of weights for the variables; ii) different sets of weights for the variables, one for each cluster (cluster-wise); iii) a double set of weights for the variables according to the decomposition of the L 2 Wasserstein distance into two components; iv) different double sets of weights for the variables and distance components, one for each cluster (cluster-wise). Applications using simulated and real data demonstrate the effectiveness of the proposed algorithms and the contribution of the relevance weights to the co-clustering procedure according to the structure of the data. [ABSTRACT FROM AUTHOR]

The growing demand of users for video streaming services has brought huge challenges to the streaming media service system. The cooperative edge-cloud computing architecture that combines cloud computing and edge computing can effectively reduce the burden on streaming media service systems. The streaming content caching under the cooperative edge-cloud computing architecture is important to improve the quality of user service. Based on this, this paper proposed a cache strategy based on user mobility and content popularity. In this cache strategy, the Markov model is used to describe the user mobility, the multiple linear regression model is used for content popularity prediction, and the content caching operation is performed according to the user mobility and the content popularity. To reduce the load of edge servers and further improve the quality of user service, this paper proposed a content placement strategy based on marginal gain. In this content placement strategy, a content placement problem is established by considering the content access latency and the content placement cost. Then a content placement algorithm based on marginal gain is proposed to solve the content placement problem. The content placement operation is performed by analyzing the marginal gain brought by placing the contents on edge servers to achieve the optimal performance of content placement. The experimental results show that the proposed strategies can effectively improve the performance in terms of the cache hit ratio, the content access latency, the storage cost and the remote data access cost. [ABSTRACT FROM AUTHOR]

Multi-label classification is a machine learning task to construct a model for assigning an entity in the dataset to two or more class labels. In order to improve the performance of multi-label classification, a multi-objective feature selection algorithm has been proposed in this paper. Feature selection as a preprocessing task for Multi-label classification problems aims to choose a subset of relevant features. Selecting a small number of high-quality features decreases the computational cost and at the same time maximizes the classification performance. However extreme decreasing the number of features causes the failure of classification. As a result, feature selection has two conflicting objectives, namely, minimizing the classification error and minimizing the number of selected features. This paper proposes a multi-objective optimization algorithm to tackle the multi-label feature selection. The task is to find a set of solutions (a subset of features) in a sophisticated large-scale search space using a reference-based multi-objective optimization method. The proposed algorithm utilizes an opposition-based binary operator to generate more diverse solutions. Injection of extreme point of the Pareto-front is another component of the algorithm which aims to find feature subsets with less classification error. The proposed method is compared with two other existing methods on eight multi-label benchmark datasets. The experimental results show that the proposed method outperforms existing algorithms in terms of various multi-objective evaluation measures, such as Hyper-volume indicator, Pure diversity, Two-set coverage, and Pareto-front proportional contribution. The proposed method leads to get a set of well-distributed trade-off solutions which reach less classification error in comparing with competitors, even with the fewer number of features. [ABSTRACT FROM AUTHOR]

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect ; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect , the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV , which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models. [ABSTRACT FROM AUTHOR]

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable. • Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions. • Propose a data-driven multi-objective optimisation algorithm. • Designing an adaptive optimiser to enhance the algorithm's depth search capability. [ABSTRACT FROM AUTHOR]

Urban rail transit networks have become the most important traffic modal and greatly improve the traffic environment and travel efficiency of metropolis, and the critical station identification and robustness assessment of urban rail transit networks have become more and more important. In this paper, we propose a comprehensive vote-rank (CVR) algorithm to identify the critical stations, and adopt mutual information and k -center clustering algorithm to analyze the similar characteristics of passenger flow on different dates of urban rail transit networks. Meanwhile, Shanghai metro network is chosen as the example and we find that the optimal classification of passenger flow on different dates is 2 categories, and the passenger flows have the significant differences between conventional day and unconventional day. The results show that the critical stations on unconventional day are farther from the city center than the critical stations on conventional day, and some critical stations locate in the suburbs on unconventional day. Moreover, we study the structural and functional robustness characteristics of urban rail transit networks subjected to continuous malicious attacks to the top critical nodes, and the results show that Shanghai metro network has the worse robustness subjected to malicious attacks. Compared with the existed methods, we can declare that the proposed CVR algorithm is the most effective method for critical station identification and robustness assessment among the discussed methods in this paper, and this study will provide theoretical and practical support for the safe operation of urban rail transit networks. [ABSTRACT FROM AUTHOR]

More training instances could lead to better classification accuracy. However, accuracy could also degrade if more training instances mean further noises and outliers. Additional training instances arguably need additional computational resources in future data mining operations. Instance selection algorithms identify subsets of training instances that could desirably increase accuracy or at least do not decrease accuracy significantly. There exist many instance selection algorithms, but no single algorithm, in general, dominates the others. Moreover, existing instance selection algorithms do not allow direct controlling of the instance selection rate. In this paper, we present a simple and generic cluster-oriented instance selection algorithm for classification problems. Our proposed algorithm runs an unsupervised K Means Clustering algorithm on the training instances and with a given selection rate, selects instances from the centers and the borders of the clusters. On 24 benchmark classification problems, when very similar percentages of instances are selected by various instance selection algorithms, K Nearest Neighbours classifiers achieve more than 2%–3% better accuracy when using instances selected by our proposed method than when using those selected by other state-of-the-art generic instance selection algorithms. [ABSTRACT FROM AUTHOR]

• Complete consistent matrix corresponding to an IFPR is constructed. • Necessary and sufficient conditions for IFPRs to be multiplicatively consistent are proposed. • Ratio-based deviation matrix that takes an accurate measurement of deviation of every element in an IFPR is constructed. • Every indeterminacy degree of an IFPR is never changed in the revising process. In this paper, we propose a method for improving the multiplicative inconsistency of an intuitionistic fuzzy preference relation (IFPR) without computing any model to derive an underlying priority weight's vector with respect to alternatives. For this, a necessary and sufficient condition for the IFPR to be multiplicative consistent is proposed and proved. Based on it, a ratio-based deviation identifying matrix that takes an accurate measurement of deviation of every element in the IFPR is constructed. We prove that the greater the value of the deviation matrix is, the more inconsistent is its corresponding element in the IFPR and based on it, a convergent iterative algorithm of improving the multiplicative consistency is presented. The algorithm uses the fact that all the indeterminacy degrees of the IFPR are never changed in the revising process of multiplicative inconsistency and as a result, the most inconsistent elements are uniquely determined by suitable elements in the IFPR, respectively. The proposed method makes a great difference from the previous methods that derived the underlying priority weight's vector with respect to alternatives based on the given IFPR for improving consistency. A numerical example is provided to show the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In the existing efficient robust model predictive control (ERMPC) algorithms (see e.g. [14,31,32]), through offline optimization and online lookup table calculation, a fixed state feedback control law or a linear interpolated control law is applied to a system when the system state lies between two adjacent polyhedrons, which undoubtedly will result in conservativeness of the controller. Faced with this issue, an improved ERMPC algorithm is proposed in this paper, which considers the nonlinearity between the state feedback control laws with respect to polyhedrons and the norm distance from system state to origin, and can provide continuously variable state feedback control law varying with the state. First, a set of polyhedral parameters and their corresponding state feedback control law sequences are obtained offline by solving a set of LMIs optimization problems. Next, for each state feedback control law sequence, a nonlinear fitting function is established offline between the state feedback control law and its serial number. Then a simplified lookup table is constructed offline to save memory space and shorten online computation time of the controller. According to the simplified lookup table and information of the norm distance from system state to origin, we online establish the coordinate of current state in the nonlinear fitting curve for getting current feedback control law, which changes continuously with the state. The proposed ERMPC algorithm is successfully applied to an actual fast-responding linear one stage inverted pendulum (LOSIP) system to verify its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid numerical development to solve two-dimensional nonlinear structural problems. The proposed approach was developed by combining weak and strong formulations and using a High-Order Development, Continuation technique and Hybrid approximation (HODC-HYB). The hybrid approximation is based on meshless strong form method and Finite Element Method (FEM). This algorithm allows us to overcome several drawbacks such as the difficulties of implementing meshless strong form methods near the boundary of the structural domain, meshless methods can be unstable and less precise for problems with Neumann boundary conditions, but these methods can overcome the connectivity technique and numerical integration in a big part of the domain. Numerical tests are carried out to demonstrate the reliability and the performance of the proposed algorithm by setting up a comparative study with the solutions obtained by HODC-FEM and HODC-MESHLESS algorithms, which are based on the weak and strong forms, respectively. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems often load in the multi-scenario environment, and they can be modeled as multi-scenario multi-objective optimization problems (MSMOs). So far, existing research on MSMOs mainly focuses on the specific method in engineering applications, and general methods are quite scarce. This paper develops an integrated information-based evolutionary framework to find a set of scenario-based non-dominated solutions for MSMOs. Sequentially, a scenario-based dominance principle evolutionary algorithm and a decomposition-based evolutionary algorithm are combined with the framework to construct two integration algorithms, respectively. Furthermore, the crucial components in both algorithms are developed under the multi-scenario environment, such as scenario-based constraint handling, scenario-based dominance concept, scenario-based crowded degree and multi-scenario decomposition method. Additionally, we design a novel comprehensive evaluation indicator for MSMOs. Finally, the proposed framework is tested on numerous artificial and engineering problems. Experiments demonstrate that the framework has superiority over four existing state-of-the-art peer competitors concerning the indicator. Two integration algorithms are able to find a set of good scenario-based non-dominated solutions. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important tasks in the field known as 'Exploratory Data Analysis' (EDA). It explores the dependencies hidden in individual data attributes, dividing them from one set into smaller subsets. In this paper, a Parallel Complete Gradient Clustering Algorithm (PCGCA) is proposed. The Complete Gradient Clustering Algorithm (CGCA) provides a natural interpretation combined with no need for assumptions regarding the number of clusters, making it an appealing choice. Moreover, in CGCA, internal optimization procedures point out the parameters influencing the size of clusters. Algorithms based on kernel density estimation can, therefore, be applied for diverse practical scenarios. Another very useful usage is outlier detection – which is especially important in the currently fast-growing data industry. The described algorithm has been validated in terms of both the speed of calculation and the quality of the obtained solution. The quality of the solution was evaluated with the use of eleven clustering indexes calculated on six data sets. In addition, the obtained result was compared with several classical well-known methods of clustering. [ABSTRACT FROM AUTHOR]

Pattern mining is one of the most studied fields in data mining. Being mostly motivated by practitioners, pattern mining algorithms are often based on heuristics and are lacking suitable formalization. In this paper, we are revisiting pattern mining, and especially itemset mining, which allows one to analyze binary datasets in searching for interesting and meaningful itemsets and respective association rules. We introduce a concise representation –the closure structure– based on closed itemsets and their minimum generators (called "passkeys") for capturing the intrinsic content of a dataset. The closure structure allows one to understand the content of the dataset in terms of closed sets and equivalence classes of itemsets. We discuss theoretical properties of passkeys which are concise representatives of closed itemsets. We propose a formalization of the closure structure and passkeys in terms of Formal Concept Analysis, which is well adapted to studying such elements. Besides theoretical results, we present the GDPM algorithm for enumerating passkeys and discovering the closure structure. GDPM is rather unique as it returns a characterization of a dataset content in terms of complexity levels, highlighting the diversity and the distribution of the itemsets. Finally, some experiments show how the GDPM algorithm and the closure structure can be practically used. [ABSTRACT FROM AUTHOR]

• A novel membrane-inspired evolutionary framework with hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. • A novel two-stage evolution strategy algorithm based on the membrane system is proposed as the algorithm in membrane. • The concept of information molecule concentration vector inspired by the binding process of information molecules and receptors is proposed to control the information transfer probability. • Can effectively avoid the negative transfer of knowledge and promote convergence. • Can achieve better performance than the state-of-the-art multitasking MOEAs. In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms. [ABSTRACT FROM AUTHOR]

Distributed optimization algorithms have the advantages of privacy protection and parallel computing. However, the distributed nature of these algorithms makes the system vulnerable to external attacks. This paper presents two penalty function based resilient algorithms for constrained distributed optimization under static and dynamic attacks. The objective function of the optimization problem is extended to nonsmooth ones and the convergence of the proposed algorithms in this case are proved under some mild conditions. Simulation experiments are performed and compared with some existing resilient primal-dual optimization algorithms using median-based mean estimator. For static attack, the proposed algorithm has better performance and faster convergence rate in the simulation experiments. For dynamic attack, the proposed algorithm has better performance and robustness in the simulation experiments, which illustrate that the proposed algorithms are more effective. [ABSTRACT FROM AUTHOR]

• An end-to-end framework which naturally incorporates KG into the recommender system. • The first KG-based recommender system combining path-based method and propagation-based method. • A method that can automatically discover the most representative user preference is proposed. • Both High-order relationship and local domain features of users/items are learned and aggregated. • The proposed algorithm achieves competitive results on three real-world datasets. Because a knowledge graph's huge amount of item information can help recommender systems develop user/item representations, it has become the most important source of side information. Regardless of the numerous types of user/item representation approaches used in knowledge graph-based recommendation scenarios, they all have problems. In this paper, we propose a knowledge graph-based multi-context-aware recommendation algorithm for learning user/item representations that combines the advantages of path-based and propagation-based methods. A new concept (i.e., rule) is proposed first, which can be a useful way to characterize the user's preferences. Next, based on user-item interactions, an automatic rule discovery algorithm is proposed that can automatically select the most representative user preferences templates in a given recommendation scenario based on the knowledge graph and user behaviors. Then, the learning of high-order connectivity between long-distance user-item pairs is realized according to these templates. After that, a feature representation method of the local neighborhood characteristics of users and items is introduced to compensate for the defect that the path-based method can only catch the high-order connectivity. The experimental results demonstrate MANN's superiority over eight state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

In the scenario of attack-defense involved with unmanned combat air vehicles (UCAVs), it is often envisioned that a large group of UCAVs is deployed to complete some complex tasks which could be specifically modeled as large-scale matrix games. Solving such matrix games by the traditional linear programming approaches, however, could be quite time-consuming and thus cannot be implemented in real-time which is, in fact, a key requirement for real air combat. On this account, an algorithm, termed as dimensionality reduction based matrix game solving algorithm (DR-MG), is proposed in this paper to solve large-scale matrix games in a timely manner. Our algorithm builds on the technique of dimensionality reduction which inherently finds the convex hull vertices of a vector set. Through establishing the connection between Nash equilibria of the matrix games before and after dimensionality reduction, the proposed algorithm is capable of finding the solutions while only dealing with the matrix game with reduced dimensions. As a consequence, it is expected the time complexity of the proposed algorithm is significantly decreased, and thus the algorithm could be applicable in real air combat. Finally, numerical results are provided to show the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem. [ABSTRACT FROM AUTHOR]

Data clustering is one of the most common and challenging problems in the machine learning domain. It requires an efficient method to be addressed. This paper proposed a new version of the Flow Direction Algorithm (FDA) to solve various optimization problems. The proposed method is called FDAOA, which enhanced the performance of the original Flow Direction Algorithm by the arithmetic operators that have been used in the Arithmetic Optimization Algorithm (AOA). The main aim of the proposed FDAOA is to avoid the recognized weaknesses in the original methods; stuck in the local area, premature convergence, and weak equilibrium between the exploration and exploitation search mechanisms. The proposed method is tested on two sets of various problems to validate its performance. In the first set, twenty-three benchmark functions are used, which belong to three categories; seven unimodal functions, six multimodal functions, and ten fixed dimension functions. In the second set, eight common data clustering problems are used to prove the ability of the proposed FDAOA to deal with real-world optimization problems. The results of the proposed method are compared with other well-established methods, and the proposed FDAOA achieved promising output compared to the other methods on various tested problems. The proposed method got the optimal clustering solutions almost in all the tested data clustering problems with clear significant improvements against the other comparative methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate some convergence results of the gradient based iterative algorithm for solving the Sylvester matrix equation A X + X B = C. We first review the development of the gradient based iterative algorithm and correct mistakes of some convergence results. Then, the idea of "two iterative factors" is applied to propose some new relaxed gradient iterative algorithms to improve the convergence performance of their previous algorithms. Finally, some numerical examples are taken to illustrate the correctness of the concluded results. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms (SAEAs) are promising methods for addressing computationally expensive problems. This paper proposes a multiple surrogates and offspring-assisted differential evolution (MSODE) algorithm for high-dimensional expensive problems. Ensemble models consisting of multiple base models are built based on bagging. The ensemble models contribute toward reducing the variations of predictions and the uncertainty of base models. The algorithm proposes a multiple-offspring evolution strategy in which it generates multiple offspring for each parent individual to enhance the search ability of the population. The appropriate number of offspring was investigated, considering the tradeoff between optimization results and efficiency. MSODE consists of a global prescreening search, local search, and uncertainty prescreening search. The global prescreening search adopts a global ensemble to prescreen promising offspring. The local search accelerates the convergence by searching for the optimum of a local ensemble. The uncertainty prescreening search requires a number of fitness evaluations that slightly deteriorate the results. A comprehensive analysis was conducted to determine the optimal parameter settings. MSODE was compared with meta -heuristic algorithms and SAEAs on a series of benchmark problems. The results show that MSODE behaves better than most algorithms and is competitive against the best ones. [ABSTRACT FROM AUTHOR]

• A new generalized fusion fractal PLFF structure is proposed. • Image cryptosystem is proposed based on new fractal structure. • Exhaustive simulation analyses validate the efficient encryption performance. The design and utilization of suitable fractal structures is one of the prominent areas of security for the protection of digital data. This paper proposes a generalized fusion fractal structure by combining two one-dimensional fractals as seed functions from a larger spectrum of fractal functions. A fusion fractal termed as PLFF is formulated by combining traditional Phoenix and Lambda fractals. Improved randomized phase space, self-similar structure on various magnification scales, and fractional dimension are found in the resultant PLFF fractal. The capacity of PLFF to create a pseudo-random number (PRN) sequence in both integer and binary format is validated by its increased complexity and enhanced chaotic range. The generated PRN sequences feature a significant degree of uncorrelation and randomness. A novel image encryption algorithm based on the new PLFF fractal function is proposed which utilizes a generated PRN sequence as secret key. Standard security evaluations such as histogram variance, NPCR and UACI tests for plain-image sensitivity, key sensitivity, information entropy, pixel correlation, and noise and data loss, etc. are used to analyze the performance of the proposed encryption algorithm. The simulation results revealed performance indicators such as entropies > 7.997, NPCR > 96.6, UACI > 33.5, high throughput of ∼ 6MBps, and highly uncorrelated neighboring pixels in encrypted images. The findings are also compared with some current image encryption schemes, demonstrating that the proposed digital image encryption algorithm performs well. [ABSTRACT FROM AUTHOR]

The paper offers a novel technique for resolving the multi-robot cooperation for stick carrying applications. The problem addresses the computation of a collision-free optimal path from a predefined initial position to target position during transportation of stick or object cooperatively by robot pair in the multi-robot environment. The stick carrying application has been resolved by embedding the modified Q-learning into the hybrid process of an improved version of particle swarm optimization and intelligent water drop algorithm. In the present context, modified Q-learning generates the best solution for particle swarm optimization and particle swarm optimization is upgraded through the perception of cubic spline and generate the optimal position in the successive iteration using intelligent water drop algorithm and also enhances the intensification and diversification capability of particle swarm optimization. The proposed hybrid algorithm computes the collision-free subsequent position for each robot pair by avoiding the obstacles in its path, avoiding the trapping at the local optima, improving the convergence speed, optimizing the path distance for every pair of robots, energy usage and path smoothness both in the static and dynamic environment's. The validation of the proposed hybrid algorithm has been verified and checked the robustness of the algorithm through computer simulation and real robots through Webots simulator. Further, the efficiency of the proposed algorithm has been verified by comparing the result obtained proposed algorithm and its competitor algorithms and comparing the result of the proposed algorithm with the existing state-of arts. The comparison result shows that the proposed algorithm is superior to its competitor algorithms and state of arts for different matrices. [ABSTRACT FROM AUTHOR]

Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence of events or transactions, called Local Periodic Patterns (LPPs) which are patterns (sets of events) that have a periodic behavior in some non predefined time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some time-interval(s). Two novel measures are proposed to assess the periodicity and frequency of patterns in time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those time-intervals have a minimum duration. To discover all LPPs, the paper presents three efficient algorithms. An experimental evaluation on real datasets shows that the proposed algorithms are efficient and can provide useful patterns that cannot be found using traditional periodic pattern mining algorithms. [ABSTRACT FROM AUTHOR]

• We propose an improved cross coupled map lattice model. • We design a bit-level image encryption scheme using the proposed model. • Bit-level confusion and diffusion are related to our model and plain-image. • The proposed method in this paper can resist the common attacks. In this paper, an improved cross coupled map lattice (CCML) which is derived from CCML is proposed. The experimental analysis of CCML shows that it exhibits weak or even lose chaotic behaviors in a large range of parameters. To enhance its chaotic performance, the new model changes the internal chaotic map of CCML from the logistics map to the tent map and introduces the module operations to limit the obtained value. The theoretical analysis and experimental results prove that the proposed model has a broad chaotic regime over an extensive range of system parameters, positive Lyapunov exponents, higher information entropies and lower mutual information values when compared to CCML, which are highly suitable for chaos-based image encryption. Moreover, this paper applies that model on a bit-level image encryption scheme. The algorithm consists of three main operations: the generation of secret keys, confusion and diffusion operations that can break the limitation of row and column. To confirm the novel algorithm's effectiveness and safety against the common types of attacks, many experiments are done and security analyses are discussed. It demonstrates the good chaos of the proposed model from the aspect of encryption performance. [ABSTRACT FROM AUTHOR]

• In this paper, proposing a novel design of DE for Knapsack problems is reported. • A dual representation of solutions in continuous and binary is introduced. • New efficient fitness evaluation and repairing method is proposed. • Results demonstrated the efficiency of Novel DE in solving Knapsack problems. • Novel DE defined new solutions, better than best-known ones for 5 large instances. The capability of the conventional differential evolution algorithm to solve optimization problems in continuous spaces has been well demonstrated and documented in the literature. However, differential evolution has been commonly considered inapplicable for several binary/permutation-based real-world problems because of its arithmetic reproduction operator. Moreover, many limitations of the standard differential evolution algorithm, such as slow convergence and becoming trapped in local optima, have been defined. In this paper, a novel technique which makes a simple differential evolution algorithm suitable and very effective for solving binary-based problems, such as binary knapsack ones, is proposed. It incorporates new components, such as representations of solutions, a mapping method and a diversity technique. Also, a new efficient fitness evaluation approach for calculating and, at the same time, repairing knapsack candidate solutions, is introduced. To assess the performance of this new algorithm, four datasets with a total of 44 instances of binary knapsack problems are considered. Its performance and those of 22 state-of-the-art algorithms are compared, with the experimental results demonstrating its superiority in terms of both the quality of solutions and computational times. It is also capable of finding new solutions which are better than the current best ones for five large knapsack problems. [ABSTRACT FROM AUTHOR]

This paper aims at solving the bipartite containment problem for linear singular multi-agent systems (MASs) with multiple dynamic leaders over general signed digraphs, where each leader can be autonomous or dynamically evolving via interactions with its neighboring leaders. To this end, we first establish some properties for the Laplacian matrices of signed digraphs. Then, three distributed observer-based bipartite containment protocols are proposed using different local output information. Multi-step algorithms for constructing the corresponding proposed protocols are also given. It is shown that under the proposed control protocols, bipartite containment can be achieved for any admissible initial states provided that the underlying signed digraph is weakly connected. Compared to the existing related works, the major contribution of present work is that the developed results are applicable for arbitrary weakly connected signed digraphs, no matter whether they are sign-symmetric or sign-asymmetric and whether they are structurally balanced or unbalanced. Two numerical examples are finally given to demonstrate the validity of our results. [ABSTRACT FROM AUTHOR]

• The outliers in the measurements are coped with the Student's t-distribution, which assigns robustness to the algorithm according to its property of heavy-tails. • The robust identification issue is solved under recursive expectation-maximization algorithm. The online updating of the parameters are realized based on a recursive Q-function. • The degree of freedom of the Student's t-distribution is online updated with the implementation of a recursive auxiliary quantity. This paper considers the robust identification issue of linear systems represented by autoregressive exogenous models using the recursive expectation-maximization (EM) algorithm. In this paper, a recursive Q-function is formulated based on the maximum likelihood principle. Meanwhile, the outliers that frequently appear in practical processes are accommodated with the Student's t-distribution. The parameter vector, variance of noise, and the degree of freedom are recursively estimated. Finally, a numerical example, as well as a simulated continuous stirred tank reactor (CSTR) system, is performed to verify the effectiveness of the proposed recursive EM algorithm. [ABSTRACT FROM AUTHOR]

Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for online and real time processing. In this paper, we address this issue by proposing a fully FPGA based hardware architecture of a popular multi-scale and multivariate signal processing algorithm, termed as multivariate empirical mode decomposition (MEMD). MEMD is a data-driven method that extends the functionality of standard empirical mode decomposition (EMD) algorithm to multichannel or multivariate data sets. Since its inception in 2010, the algorithm has found wide spread applications spanning different engineering related fields. Yet, no parallel FPGA based hardware design of the algorithm is available for its online and real time processing. Our proposed architecture for MEMD uses fixed-point operations and employs cubic spline interpolation (CSI) within the sifting process. Finally, examples of decomposition of multivariate synthetic and real-world biological signals are provided. [ABSTRACT FROM AUTHOR]

Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation. [ABSTRACT FROM AUTHOR]

Active learning method (ALM) is a powerful fuzzy–based soft computing methodology suitable for various applications such as function modeling, control systems, clustering and classification. Despite considerable advantages, the main computational engine of ALM, ink drop spread (IDS), is memory-intensive, which imposes significant area overheads in the hardware realization of the ALM for real–time applications. In this paper, we propose a compressed model for ALM which greatly alleviates the storage limitations. The proposed approach employs a distinct inference algorithm, enabling a significant reduction in memory utilization from $O(N^{2})$ to $O(2N)$ for a multi–input single–output (MISO) system. Also, the computational costs in both training and inference modes are decreased to only a few additions and multiplications. Furthermore, we develop a memory–efficient digital architecture for the proposed compressed ALM algorithm that can be leveraged for various computing systems through configuring a few registers. Finally, we assess the performance of the proposed approach using various function modeling and classification applications and provide a comparison with conventional ALM and some other well-know approaches. Simulation and hardware implementation results demonstrate that the proposed approach achieves reduced noise sensitivity with $128\times $ reduction in the average memory usage while realizing comparable accuracy compared to the other approaches studied herein. [ABSTRACT FROM AUTHOR]

This paper provides a framework of designing distributed model predictive controller to reach consensus of a heterogeneous time-varying multi-agent system, and the dynamics of heterogeneous agents are modeled by double integrators and Euler-Lagrange (EL) equations. Firstly, a DMPC-based consensus algorithm is proposed, where the constraints in the algorithm depend on the heterogeneous dynamics. We prove that the resultant DMPC optimization problem is feasible with the designed controllers, which is stable when the system reaches consensus. To further reduce communication cost and solve the problem with asynchronous discrete-time information exchange, self-triggered mechanism is introduced into the framework. Trigger intervals are alternatively optimized with the control inputs, and the influence on the system performance is analyzed. Numerical examples are provided to verify the effectiveness and advantages of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Integrating type analog-to-digital converters (ADC) used in column-parallel CMOS image sensors trade conversion speed with size, power, and complexity to achieve optimal performance. A new integrating ADC architecture called single-slope look-ahead ramp (SSLAR) ADC is introduced in this paper. It utilizes a statistical approach and code-prediction methods to improve the conversion speed of standard single-slope ramp (SSR) ADC. It is shown that SSLAR ADC reduces power consumption while achieving an increased frame rate. This is achieved by the SSLAR algorithm that was optimized for column-parallel CMOS active pixel sensor (APS) imager architecture. A 10-bit SSLAR ADC was designed in a $0.5~\mu \text{m}$ CMOS (2P3M) process and integrated with a column-parallel CMOS image sensor that has $200\times 150$ array with $15~\mu \text{m}$ pixels. Measurements showed that a 6x frame rate increase can be achieved while reducing power consumption 13% with minimal impact on image quality. [ABSTRACT FROM AUTHOR]

This paper presents an area-efficient architecture for stochastic low-density parity-check (LDPC) decoder with high throughput and excellent bit-error-rate (BER) performance. The correlation effects of a stochastic Sum-Product Algorithm (SPA) are analyzed. Based on this analysis, a variable node (VN) structure is proposed and its similarity with a correlation divider (CORDIV) is pointed out. Based on the properties of CORDIV, the area of probability tracer in the VN is reduced significantly. In order to achieve more accurate results when the check-to-variable (C2V) messages are not strong enough, the 3-3 input grouping sub-node is replaced by an adder-based 5-1 input grouping sub-node of the degree-6 VN for (2048,1723) code. An unbiased stochastic sequence generator is adopted to get more accurate results from the smaller probability tracer. Furthermore, the soft bit-flipping prior-processing and the C2V-based hard decision updating method are combined in VN to reduce the decoding latency. A (2048,1723) stochastic LDPC decoder is designed in the TSMC 65 nm process to demonstrate the proposed decoder architecture. With the aid of early termination, the decoder occupies 2.34 mm2 core area and can achieve 116.17 Gb/s at 4.4 dB and 461.99 Gb/s at 5.5 dB under 970 MHz with better decoding performance. Compared with the state-of-the-art stochastic IEEE 802.3an LDPC decoders, the proposed architecture can achieve the best throughput, throughput-to-area ratio, and BER performance. [ABSTRACT FROM AUTHOR]

Due to non-linear factors such as the rate capacity and the recovery effect, the shape of the battery discharge curve plays a significant role in the overall lifetime of the batteries. Accordingly, this paper proposes a simple heuristic battery-aware speed scheduling policy for periodic and non-periodic real-time tasks in Dynamic Voltage Scaling (DVS) systems with non-negligible leakage/static power. A set of comprehensive analysis has been conducted to compare the battery efficiency of the proposed policies with an optimal solution, which could be derived via the Calculus of Variations (CoV). These evaluations have taken into account both periodic and non-periodic tasks in DVS-based systems. Our experiments have shown a maximum of 7% difference between the optimal solution and the simple heuristic speed scheduling for realistic settings of the battery model. By considering the calculated optimal speed scheduling for different tasks (with different utilizations), a two-phase algorithm has been proposed, in which a speed approximation function is being calculated offline based on curve fitting, while the best execution speed is applied online. The results show a maximum of 17.7% and 11.3% battery charge saving for non-periodic and periodic tasks in comparison to the baseline critical frequency method, respectively. [ABSTRACT FROM AUTHOR]

Isogeny-based cryptography is one of the promising post-quantum candidates mainly because of its smaller public key length. Due to its high computational cost, efficient implementations are significantly important. In this paper, we have proposed a high-speed FPGA implementation of the supersingular isogeny Diffie-Hellman (SIDH) and key encapsulation (SIKE). To this end, we have adapted the algorithm of finding optimal large-degree isogeny computation strategy for hardware implementations. Using this algorithm, hardware-suited strategies (HSSs) can be devised. We have also developed a tool to schedule field arithmetic operations efficiently using constraint programming. This tool enables reducing the latency of SIDH and SIKE subroutines by up to 14% at NIST’s highest security level, i.e., using the SIKEp751 parameter set. We have also improved the latency of field inversion, the most costly field operation in SIDH, by 23% using the Montgomery ladder technique. We have provided constant-time implementations of SIDH and SIKE on Virtex-7 using SIKEp751 utilizing 6 and 8 prime field multipliers to resemble the previous work. Experimental results show that using 8 multipliers SIDH and SIKE encapsulation and decapsulation can be performed in 24.66 ms and 24.10 ms, which is 1.37 and 1.12 times faster than the latest corresponding FPGA implementations, respectively. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced optimal control technique based on adaptive Maximize-M Kalman filter (AM-MKF) is used. To maximize power extraction from solar PV (Photovoltaic) panel, a learning-based hill climbing (L-HC) algorithm is implemented for a grid integrated solar PV system. For the testing, a three-phase system configuration based on 2-stage topology, and the deployed load on a common connection point (CCP) are considered. The L-HC MPPT algorithm is the modified version of HC (Hill Climbing) algorithm, where issues like, oscillation in steady-state condition and, slow response during dynamic change condition are mitigated. The AM-MKF is an advanced version of KF (Kalman Filter), where for optimal estimation in KF, an AM-M (Adaptive Maximize-M) concept is integrated. The key objective of the novel control strategy is to extract maximum power from the solar panel and to meet the demand of the load. After satisfying the load demand, the rest power is transferred to the grid. However, in the nighttime, the system is used for reactive power support, which mode of operation is known as a DSTATCOM (Distribution Static Compensator). The capability of developed control strategies, is proven through testing on a prototype. During experimentation, different adverse grid conditions, unbalanced load situation and variable solar insolation are considered. In these situations, the satisfactory performances of control techniques prove the effectiveness of the developed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes the Lanczos version of the biconjugate residual algorithm to solve the reflexive or anti-reflexive solutions of a class of generalized coupled Sylvester matrix equations. We give a convergence analysis, and by constructing a special form of the initial matrices, the minimum-norm reflexive solutions can be obtained through finite-step iterations without considering rounding errors. At the end of the paper, we provide numerical examples to illustrate the feasibility and effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

Aiming at reducing the complexity of minimum mean square error (MMSE) detection in massive multiple-input multiple-output (MIMO) systems, this paper proposes a detection algorithm with high convergence rate and an efficient hardware architecture based on second-order Richardson iteration (SORI). In the proposed algorithm, a pre-iteration-based initialization method is presented to accelerate the convergence without extra complexity. In addition, the approximation of relaxation factor and the log-likelihood ratio (LLR) is introduced to further reduce computing load. Theoretical analysis demonstrates the advantages of the proposed algorithm in fast convergence and low complexity, and simulation results show that the proposed algorithm can efficiently approach MMSE performance. Based on this algorithm, a flexible hardware architecture is designed, which is deeply pipelined to support $128\times U$ ($8\leq U\leq 32$) massive MIMO detection with the configurable number of iterations, and a folded dual-mode systolic array (DMSA) is fully utilized to achieve the flexibility with low hardware consumption. Implemented on Xilinx Virtex-7 FPGA and SMIC 40nm CMOS technology, the proposed detector is competitive in terms of energy and area efficiency compared to state-of-the-art iterative detectors, and it can adapt to the varied channel condition and the number of users in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Single constant multiplication (SCM) and multiple constant multiplications (MCM) are among the most popular schemes used for low-complexity shift-add implementation of finite impulse response (FIR) filters. While SCM is used in the direct form realization of FIR filters, MCM is used in the transposed direct form structures. Very often, the hybrid form FIR filters where the sub-sections are implemented by fixed-size MCM blocks provide better area, time, and power efficiency than those of traditional MCM and SCM based implementations. To have an efficient hybrid form filter, in this paper, we have performed a detailed complexity analysis in terms of the hardware and time consumed by the hybrid form structures. We find that the existing hybrid form structures lead to an undesirable increase of complexity in the structural-adder block. Therefore, to have a more efficient implementation, a variable size partitioning approach is proposed in this paper. It is shown that the proposed approach consumes less area and provides nearly 11% reduction of critical path delay, 40% reduction of power consumption, 15% reduction of area-delay product, 52% reduction of energy-delay product, and 42% reduction of power-area product, on an average, over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a chaotic image encryption algorithm based on the matrix semi-tensor product (STP) with a compound secret key is designed. First, a new scrambling method is designed. The pixels of the initial plaintext image are randomly divided into four blocks. The pixels in each block are then subjected to different numbers of rounds of Arnold transformation, and the four blocks are combined to generate a scrambled image. Then, a compound secret key is designed. A set of pseudosecret keys is given and filtered through a synchronously updating Boolean network to generate the real secret key. This secret key is used as the initial value of the mixed linear-nonlinear coupled map lattice (MLNCML) system to generate a chaotic sequence. Finally, the STP operation is applied to the chaotic sequences and the scrambled image to generate an encrypted image. Compared with other encryption algorithms, the algorithm proposed in this paper is more secure and effective, and it is also suitable for color image encryption. [ABSTRACT FROM AUTHOR]

1 This paper proposes an adaptive clustering-based many-objective evolutionary algorithm. 2 A unit hypersurface is estimated to roughly reflect the characteristicsof Pareto fronts. 3 The adaptive clustering can properly fit various Pareto frontsforthe targetproblem. 4 The proposed algorithmimprovestheperformanceondifferentproblemswith various Pareto fronts. This paper proposes an adaptive clustering-based evolutionary algorithm for many-objective optimization problems (MaOPs), called MaOEA/AC. In this algorithm, an adaptive clustering strategy (ACS) is first introduced to divide the population into multiple clusters, which can properly fit various Pareto fronts (PFs) of the target MaOPs. Then, the environmental selection of MaOEA/AC is designed based on these clusters to collect the solutions with balanceable convergence and diversity. To be more detail, the similarity between solutions in ACS is appropriately measured by computing the Euclidean distance between their projections on an adaptive unit hyper-surface, whose curving rate is controlled by a parameter p. A simple yet effective estimation method is proposed to get a suitable value of p based on the distribution of the current non-dominated solution set, so that the estimated unit hyper-surface can roughly reflect the characteristics of PFs in the target MaOPs. The effectiveness of MaOEA/AC is validated by numerous experimental studies on solving test MaOPs with various PFs, which have the characteristics with convex, concave, inverted, disconnected, degenerated, and other mixed or irregular PFs. The experiments also show that MaOEA/AC has the superior performance over several recent many-objective evolutionary algorithms, when solving most of these test MaOPs. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) has been widely studied due to its high performance and usability for various applications such as image classification, detection, segmentation, translation, and action recognition. Thanks to the universal applications and high performance of DNN algorithm, DNN is adopted for various AI platforms, including edge/mobile devices as well as cloud servers. However, high-performance DNN requires a large amount of computation and memory access, making it challenging to implement DNN operation on edge/mobile. There have been several ways to solve these problems, including algorithms as well as hardware for DNN. Algorithms that help accelerate DNN in hardware enable much more efficient operation of high-performance AI. This article aims to provide an overview of the recent hardware and algorithm co-design schemes enabling efficient processing of DNNs. Specifically, it will provide algorithm optimization methods for DNN structure, neurons, synapses, and data types. This paper also introduces optimization methods for hardware architectures, PE array, data-path control, and microarchitecture of PE. And we will also show examples of DNN algorithm and hardware co-designed ASICs. [ABSTRACT FROM AUTHOR]

Sparsified kernel adaptive filters (SKAFs) is an attractive filtering solution with low memory and computational complexity. Most of existing SKAFs are based on the mean square error (MSE) criterion under Gaussian noise assumption for its simplicity and convenience. When the assumption deviates largely from the underlying truth, the performance of these methods could degrade significantly. In this paper, we propose a novel SKAF, named as projected kernel least mean p-power algorithm (PKLMP), based on the mean p-power error (MPE) criterion and vector projection (VP) method. We provide convergence analyses in terms of the stead-state MSE, based on a Taylor expansion method, and derive the lower and upper bounds for the steady-state excess MSE. We also conduct mean convergence analysis for PKLMP, and derive convergence conditions. To exploit the information in the desired outputs, we further derive a modified PKLMP by smoothing the desired signal. Finally, a simple and effective online variable kernel centers strategy is proposed to improve the filtering performance of the proposed KAFs. Simulation results under a static function estimation, a chaotic time-series prediction, and two real-world time-series predictions are conducted and validate the effectiveness of the proposed PKLMP algorithms. [ABSTRACT FROM AUTHOR]

Piecewise affine (PWA) models are attractive frameworks that can represent various hybrid systems with local affine submodels and polyhedral regions due to their universal approximation properties. The PWA identification problem amounts to estimating both the submodel parameters and the polyhedral partitions from data. In this paper, we propose a novel approach to address the identification problem of PWA systems such that the number of submodels, parameters of submodels, and the polyhedral partitions are obtained. In particular, a cluster-based algorithm is designed to acquire the number of submodels, the initial labeled data set, and initial parameters corresponding to each submodel. Additionally, we develop a modified self-training support vector machine algorithm to simultaneously identify the hyperplanes and parameter of each submodel with the ouputs of the cluster-based algorithm. The proposed algorithm is computationally efficient for region estimation and able to accomplish this task with only a small quantity of classified regression vectors. The effectiveness of the proposed identification approach is illustrated via simulation results. [ABSTRACT FROM AUTHOR]

For the bilinear stochastic system, the difficulty of identification lies in the product of the state vector and input in the system. This paper studies the iterative estimation of the parameters and states for the bilinear state-space systems in the observer canonical form. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. Therefore, this paper proposes a state filter (SF) for the bilinear systems based on the extremum principle. By means of the hierarchical principle, we decompose the identification model into two sub-identification models by introducing two fictitious output variables. Then an SF two-stage gradient-based iterative algorithm is proposed to achieve the combined parameter and state estimation according to the gradient search. For the purpose of improving the identification performance, an SF two-stage moving data window gradient-based iterative algorithm is derived by increasing the data utilization. The numerical example demonstrates the validity of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In time-delay two-dimensional (2-D) systems, the variables not only depend on time but also on spatial coordinates, moreover, more than one input data are subjected to time-delays at each sampling time. In order to overcome these difficulties, this paper develops an expectation maximization (EM) identification algorithm for estimating the 2-D systems. Compared with the traditional compressed sensing recovery algorithm and the redundant rule based estimation algorithm, the EM algorithm in this paper has three integrated key functions, (1) to estimate the parameters and the unknown time-delays simultaneously, (2) to keep the number of the unknown parameters unchanged, (3) to identify the 2-D systems with varying time-delays. The simulations made further guarantees the usefulness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present a modified MEC algorithm for demagnetization modeling of the PM motor. One of the major issues that the designers of the permanent magnet (PM) motors are faced with is the demagnetization of magnets because of high temperatures and armature reaction. Demagnetization will weaken the magnetic properties of the magnet and lead to a reduction in the performance of the motor. Therefore, it is essential to provide appropriate methods for modeling this phenomenon. One of these methods that has a compromise between accuracy and time consumption is the magnetic equivalent circuit (MEC). In this paper, the MEC method is used for modeling the demagnetization phenomenon for the newly introduced ring winding axial flux PM (RWAFPM) motor. The proposed algorithm can take the demagnetization into account through a time-stepping model and also correct the value of the knee point flux density. Design/methodology/approach: The modified MEC method is used for demagnetization modeling. The modified algorithm can take into account demagnetization and also renew the knee point at each step to increase the accuracy of the modeling. In addition, the proposed algorithm has a very high and fast execution speed so that the computation time of the MEC algorithm compared to the FEM model is reduced from 3 h to 35 s. In this case, the simulations have been performed on a core i5@ 2.3 GHz/8GB computer. The FEM model is used to verify the validity of the MEC results. Findings: The obtained results show that at the high temperature, RWAFPM motor is severely vulnerable to demagnetization. At the temperature of 140°C, the demagnetization rate of 35% has occurred. So, it is necessary to use the high-temperature magnet in this motor or modify the motor structure in terms of demagnetization tolerant capability. Originality/value: The RWAFPM motor is introduced for use in ship propulsion and traction systems. For this reason, an accurate estimation of demagnetization tolerant of this motor in different working conditions can show the strengths and weaknesses of this structure. [ABSTRACT FROM AUTHOR]