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In the application of interval type-2 (IT2) Takagi–Sugeno–Kang (TSK) fuzzy logic systems (FLSs), the center-of-sets (COS) type-reduction (TR) is more advantageous than the centroid TR. This paper proposes three types of discrete non-iterative algorithms to solve the problem of COS TR in IT2 TSK FLSs. Multiple simulation experiments are carried out for the IT2 TSK FLSs with different fuzzy rule numbers. Experimental results show that the computational efficiencies of the three discrete non-iterative algorithms are better than that of Karnik–Mendel (KM) algorithms, which provides latent value for the application of type-2 FLSs. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL) has been an active topic in artificial intelligence and cognitive computing. Two-way concept-cognitive learning (TCCL), an efficient cognitive mechanism to discover knowledge from an arbitrary information granule, has attracted attention to the problems of concept learning. Although TCCL has been widely adopted for cognitive and learning concepts, the existing studies still have some issues: learning concepts can only from the standard formal context, i.e., extent with discrete information, and ignoring the fuzziness of object information that is also important to precise cognitive and learning concepts. Hence, this paper proposes a novel L-fuzzy concept learning method via two-way concept-cognitive learning and fuzzy residual implication, namely, L-fuzzy concept-cognitive learning (LF-CCL). Specifically, we give the theoretical underpinnings of the L-fuzzy CCL method and a pair of dual cognitive operators are presented based on residual implication. Then L-fuzzy two-way granule spaces are constructed to explore the transformation of information granules and the generation of knowledge granules from arbitrary cognitive clues. Meanwhile, we analyze the CCL progress using L-fuzzy operators based on fixed points. Moreover, the corresponding algorithms are designed, and the decision value of this method in financial investment applications is explored through experimental analysis. Finally, numerical experiments verify the effectiveness and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

A sensorless control method of permanent magnet synchronous motor (PMSM) based on adaptive super-twisting algorithm sliding mode observer (STASMO) is proposed in this paper. The traditional sliding mode observer (SMO) algorithm has the problem of inherent high-frequency chattering due to the use of switching function. The phase delay will be caused by using low-pass filter to deal with the problem of high-frequency buffeting. In this paper, the chattering of the system is effectively suppressed by combining the super-twisting algorithm and the SMO algorithm. The chattering suppression ability of the algorithm is further improved by adding an adaptive coefficient associated with speed in front of the higher-order integral term of the observer. At the same time, the speed and rotor position information are extracted by normalized phase-locked loop (PLL), which avoids the use of low-pass filter and phase compensation module. In order to overcome the influence of the change of motor parameters on the control system, an online estimation method of motor parameters is proposed. The values of stator resistance and stator inductance are estimated online in real time, and the estimated values are fed back to the SMO, which improves the system performance and estimation accuracy. Through simulations and experiments, it is proved that the proposed algorithm can effectively suppress high-frequency chattering, effectively improve the estimation performance of PMSM sensorless control system, and obtain more accurate speed and rotor position information. [ABSTRACT FROM AUTHOR]

Atanassov intuitionistic fuzzy set (AIFS)-based C-means algorithms are successful in clustering uncertain or vague real-world datasets. The AIFS-based clustering algorithms are classified into adaptive class and non-adaptive class. An algorithm from the adaptive class computes its feature weight distribution with the help of the given dataset. On the other side, the algorithm belonging to the non-adaptive class mostly computes the feature weight distribution by employing an equally likely approach. The guarantee to reach up to the mark clustering performance is missing within this approach. Simultaneously, the performance gets deteriorated if the datasets showcase noises/irrelevant features. The irrelevant features in the datasets add to the computational cost. So, a feature reduction-equipped clustering algorithm called uni-weighted intuitionistic fuzzy C-means (uW-IFCM) is introduced in the paper. Moreover, the probabilistic weights-based adaptive clustering algorithm, namely bi-weighted probabilistic intuitionistic fuzzy C-means (bW-PIFCM) is proposed under the AIFS environment. The parametric analysis for uW-IFCM is provided to comprehend and compare its performance with bW-PIFCM, PIFCM, IFCM, and FCM algorithms. Here, an intuitionistic data fuzzification technique transforms the real-valued dataset into AIFS dataset, therefore bW-PIFCM and uW-IFCM algorithms cluster the real-valued datasets. The research proposal of Yang and Nataliani in [IEEE Transactions on Fuzzy Systems, 26(2), 817–835] motivates us to introduce a feature reduction-equipped uW-IFCM algorithm. We have considered synthetic datasets and some UCI machine learning datasets for the experimental study of uW-IFCM and bW-PIFCM. The efficacy and the precision of proposed algorithms are tested in terms of some popular benchmark indexes as well. [ABSTRACT FROM AUTHOR]

The adaptive neuro-fuzzy inference system (ANFIS), an efficient soft computing approach, has both high interpretability and self-learning ability. ANFIS can effectively handle the both classification and regression of low-dimensional data, but it deals poorly with high-dimensional big data due to the curse of dimensionality. This paper proposes a highly interpretable deep neural-fuzzy system (DNFSA)-based algorithm to efficiently and effectively train fuzzy classifiers. It integrates several novel techniques: (1) independent membership functions (MFs) and fuzzy c-means clustering, which initialize rule by fuzzy c-means clustering to train Takagi–Sugeno–Kang fuzzy system efficiently and (2) maximal information coefficient, which identifies interesting relationships between pairs of variables, so as to enhance the interpretability and generalization performance. Specifically, by viewing improved ANFIS as the base learner, we construct DNFSA in the fashion of parallel layer by layer. A visualized structure can be obtained automatically and presented to the users for better understanding of DNFSA. Furthermore, the proposed DNFSA can effectively determine which base learners should be abandoned from a set of available sub-fuzzy systems, which reveals that it may be better to ensemble many sub-fuzzy systems instead of all at hand. Experiments are conducted to confirm the DNFSA can significantly reduce the rules along with the parameters to have better interpretability. [ABSTRACT FROM AUTHOR]

With the growing popularity of movies as a source of entertainment and relaxation in modern society, movie recommendation systems have become increasingly important for helping viewers navigate a vast selection of movie products. However, existing methods may not accurately capture the preferences and opinions of viewers. To address this gap, we propose a novel approach that utilizes the Pythagorean hesitant fuzzy distance measure in combination with the VIKOR method to deal with the extracted review information and evaluate the similarity between different movies in a given genre. We then transform this information into Pythagorean hesitant fuzzy attribute evaluation terms using the Probabilistic Linguistic Term Set (PLTS) and apply conflict degree theory to calculate similarity scores. The algorithm proposed in this paper employs the VIKOR method to select recommended movie products that match the preferences of users and satisfy their needs. Comparative analysis demonstrates its reliability and stability. In addition, according to the comparative data, its performance is better than the comparable method in the selection of alternative schemes, which highlights its contribution in the field of movie recommendation systems. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy dual-hunting control based on auction algorithm is proposed. Relying on a single containment gives the small target the possibility of escape. Therefore, we design a dual-hunting framework for multiagent systems to hunt a small target. A hunting formation with double containment is designed to reduce the possibility of the target escaping. Then, an auction algorithm is designed to generate the desired formation and rationally plan the positions of the agents in the hunting formation. To improve the applicability of the dual-hunting framework, we take high-order multiagent systems as the research object, and the fuzzy logic system (FLS) is introduced to approximate the unknown nonlinear dynamics (UND) due to higher-order system model errors and environmental disturbances. Based on FLS and backstepping method, a hunting controller for high-order systems is designed, which enables multiagent systems to track targets and form hunting formations. Furthermore, the Lyapunov function is designed to prove the stability of the controller. Finally, the effectiveness of the proposed method is demonstrated by simulating a multiagent system containing 12 intelligences hunting for a dynamic target. [ABSTRACT FROM AUTHOR]

This paper discusses the design and implementation of a Robust Shrine Affine Projection Sign (RSAPS) adaptive control algorithm for power quality improvement. Various limitations of conventional 2-level inverters can be overcome using a 5-level Cascaded H-Bridge multilevel inverter (CHB-MLI). Such a CHB-MLI is realized and controlled as a distribution static compensator (DSTATCOM) unit in this paper. The DC link of capacitors have been maintained to reference value by using proportional-integral (PI) controllers without using complex balancing circuits. Extensive simulation has been performed and a scaled -down experimental prototype model has been developed in the laboratory to test the system under steady state and dynamic load conditions using dSPACE-1104 digital signal processor. The simulation and experimental results show the source current harmonics, DC voltage regulation, power factor correction, weight updation etc. under varying load. The proposed algorithm gives satisfactory performance in terms of weight convergence, computational burden, mean square error (MSE) etc. under dynamic load conditions and in addition the THD obtained in source voltage and current is within acceptable IEEE-519 limits. [ABSTRACT FROM AUTHOR]

More and more multi-objective evolutionary algorithms are proposed and used to solve many-objective optimization problems and large-scale optimization problems. However, most of the existing algorithms use fixed crossover probability (pc) and mutation probability (pm) in the generation process of offspring, which makes the performance of the algorithm poor when dealing with complex problems. In this paper, by analyzing the complex non-linear relationship between performance metrics and pc and pm in the search process of many-objective evolutionary algorithms. A fuzzy inference system is constructed to dynamically update the pc and pm in the iterative process. Therefore, a fuzzy adaptive NSGA-III algorithm is proposed and used to solve large-scale optimization problems. For the construction of fuzzy systems, this paper takes the number of iterations, convergence metric, and diversity metric as inputs, and pc and pm as outputs. Four different fuzzy systems are obtained, and the best fuzzy system is selected through experiments. In order to further verify the effectiveness of the algorithm, the proposed algorithm and the existing literature are tested on LSMOP problems. The results show that the fuzzy system can well describe the complex non-linear relationship between the performance metrics and the pc and the pm in the search process of the many-objective evolutionary algorithm. It also effectively improves the performance of the algorithm when solving large-scale optimization problems, resulting in maintenance of the convergence and diversity of the population. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the TextRank method (TM) which only considers the co-occurrence between words and the incipient word importance when extracting keywords, this paper proposes a tolerance rough set (TRS)-based unsupervised keyword extraction method. Generally, how to score the words in a document has a significant influence on the word graph modeling. In this paper, we improve TM in two aspects with TRS theory that is used to mine vocabulary, semantics, grammar and other information in the corpus. First, the degree of words belonging to each document is calculated to form a fuzzy membership matrix, which helps to characterize the incipient word importance. Second, the fuzzy membership of words to each word tolerance class is calculated to form a semantic correlation matrix, which contributes to optimize the transition probability of all graph edges. We apply the proposed methods to the clustering tasks of two datasets, outperforming the strong baselines. [ABSTRACT FROM AUTHOR]

It has become quite common in recent early oncology trials to include both the dose-finding and the dose-expansion parts within the same study. This shift can be viewed as a seamless way of conducting the trials to obtain information on safety and efficacy hence identifying an optimal dose (OD) rather than just the maximum tolerated dose (MTD). One approach is to conduct a dose-finding part based solely on toxicity outcomes, followed by a dose expansion part to evaluate efficacy outcomes. Another approach employs only the dose-finding part, where the dose-finding decisions are made utilizing both the efficacy and toxicity outcomes of those enrolled patients. In this paper, we compared the two approaches through simulation studies under various realistic settings. The percentage of correct ODs selection, the average number of patients allocated to the ODs, and the average trial duration are reported in choosing the appropriate designs for their early-stage dose-finding trials, including expansion cohorts. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is widely used in image segmentation because of its ability to describe the uncertain information presented in images. However, traditional fuzzy clustering ignores the spatial contextual information in the image, which makes it poor in processing images corrupted by high noise. In this paper, we present a novel fuzzy clustering algorithm called PFLWCM-CIM algorithm for noise image segmentation by introducing the image patches, the correntropy induced metric (CIM), and a fuzzy local weighted factor. Firstly, we use image patches as samples in clustering. Compared with individual pixels, image patches can preserve the local geometry of images by considering comprehensive features. Secondly, a new distance measure is developed on the basis of image patches and CIM to describe the relationship between samples and cluster centers. The CIM is more robust to noise than the traditional L 2 -norm. Next, a novel weighting method to characterize the similarity between two pixels named local weights is proposed. The local weights combine the spatial location relationship and the pixel value relationship of two pixels simultaneously, which describes the relationship between pixels from a more reasonable perspective. Furthermore, a new fuzzy local weighted factor is put forward by integrating the new distance measure and the local weights, then the PFLWCM-CIM algorithm is proposed based on the new factor and the idea of image patches. Several commonly used fuzzy clustering algorithms are incorporated into the experiments in segmenting images polluted by various types of noise. Experimental results demonstrate that our algorithm has reached state-of-the-art results in several metrics. [ABSTRACT FROM AUTHOR]

Reconfiguration techniques play an essential role in maximum power enhancement from PV arrays under partial shading conditions. Reconfiguration techniques are of two types based on the electrical connection. If the electrical connection remains the same after the rearrangement of panels, then that reconfiguration is static; otherwise, it is dynamic. Out of all the static reconfiguration techniques, the fixed column reconfiguration technique is the most flexible method. For static reconfiguration, the length of the column wire should be more than the usual connection. This paper proposes an algorithm for finding the column wiring resistance for fixed column static reconfiguration of a PV array. The proposed algorithm is applied to reconfigure Total-Cross-Tied (T-C-T) and Triple-Tied-Cross-Linked (T-T-C-L) PV array configurations. The performance of T-C-T and T-T-C-L PV arrays is analyzed in four reconfigured patterns by considering cross-ties resistance and column wiring resistance under dynamic shading conditions. [ABSTRACT FROM AUTHOR]

The penetration of distributed generation (DG) in the distribution network has become a necessity and a significant solution to improve power grid quality, and solve power losses issue. To reach these targets, integrating these DGs in an optimal placement with an optimal sizing should be investigated and taken into consideration. This paper focuses on obtaining the optimal allocation and size of a photovoltaic (PV) distributed generation (PVDG) in order to reduce the total power losses and enhance voltage and frequency profiles of a modified IEEE 14 node distribution network (Hooshmand in J Appl Sci 8(16):2788–2800, 2008, https://doi.org/10.3923/jas.2008.2788.2800). An objective function is used in this paper aims to reduce grid power losses, and two optimization algorithms are applied to solve this function which are the particle swarm optimization (PSO) and the genetic algorithm (GA). Added to that, two scenarios are discussed in this paper in order to analyze the effects of variable PV penetration level, hourly load consumption profile variation and atmospheric condition change on the sizing optimization resolution. The obtained simulation results prove that the PSO algorithm has better performance compared to the GA in terms of speed convergence, power loss reduction and grid quality improvement (voltage and frequency profiles). Then, it shows that variable load consumption curve and weather condition change can affect not only the determination of the PVDG optimal position and capacity but also grid security. [ABSTRACT FROM AUTHOR]

This paper intends to propose a power quality design model for the distributed system through nonlinear functions, and hence the prerequisite of power quality enhancements can be precisely quantified. As the model is adaptable, it needs a robust optimization algorithm for estimating the optimal location and compensation of the D-STATCOM. Hence, this paper develops a hybrid meta-heuristic optimization algorithm based on the prey targeting behavior of whales. The proposed hybrid whale optimization, Whale with Grey Wolf Optimization (WG), is used for determining the optimal placing and sizing of D-STATCOM by solving the power quality model. The solutions will be reactive power-encoded with two bound constraints to address both the localizing and sizing problems. Besides, along with the renowned literature, we determine the Mean Voltage Stability Index. The updating algorithm of the whale optimization will be hybridized with the hunting behavior of grey wolves so that the location and sizing of D-STATCOM can be estimated precisely. The proposed WG algorithm compares its performance over other conventional methods such as GA, ABC, PSO, GWO, and WOA in terms of convergence analysis, cost analysis, and total loss and determines the effectiveness of the proposed power quality model. [ABSTRACT FROM AUTHOR]

This paper addressed the robust fuzzy C-Means design for a class of clustering algorithm that are robust against both the plant parameter perturbations with nonlinearity and controller gain variations. Based on the description of Takagi–Sugeno (TS) fuzzy model, the stability and control of nonlinear systems are studied. The recently proposed integral inequality is selected based on the free weight matrix, and the minimum conservative stability criterion is given in the form of linear matrix inequality (LMI). Assuming that the controller and the system have the same premise, this method does not require the number and membership function rules. In addition, the improved control is used as the stability criterion of the closed-loop TS fuzzy system obtained from LMI in large-scale nonlinear systems, and is reorganized for machine learning. The novelty of this paper is to develop a simplified and robust controller design for a class of nonlinear perturbed systems. Moreover, the proposed control process was also ensured by the control criterion derived from the energy function for the stability of the nonlinear system. Finally, a simulation is given and demonstrated the feasibility of the practical application motivated by certain concrete-real problem in vibrated structures. [ABSTRACT FROM AUTHOR]

In this paper, based on the modified fuzzy c-regression model and noise clustering algorithm, a fuzzy identification method is proposed. Firstly, by considering the relations for the real model, the established model, and the outliers, a modified objective function with noise is proposed to alleviate the affection of noise. Additionally, the consequent parameters of the fuzzy model can be obtained by the iterative formula which obtained by the Lagrangian formula. Furthermore, a modified membership function, which is involved the likelihood function, is propounded to get a more suitable multivariate normal distribution for the data points. Lastly, two examples are illustrated to show the validity and effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive anti-noise least-squares algorithm (ANLS) is proposed for parameter identification of an unmanned marine vehicle in the presence of measurement noise. As a basis, a horizontal-plane second-order nonlinear Nomoto model is established and transformed into a discrete-time model for parameter identification. Then, a noise reduction term is added to the loss function to achieve a trade-off between the anti-noise effect and parameter identification accuracy. Furthermore, the Levenberg–Marquardt algorithm is embedded into the parameter identification algorithm to achieve adaptive coefficient optimization. Finally, the simulation and experimental data are utilized for parameter identification and performance validation. By comparing with the recursive least-squares algorithm and least-squares support vector machine algorithm, the excellent anti-noise and maneuvering prediction abilities of the proposed ANLS algorithm are verified, i.e., up to 84% reduction of the identification error in the simulation and less than 4 ∘ of the heading angle prediction error in the experiment. [ABSTRACT FROM AUTHOR]

This paper presents a binocular stereo calibration algorithm using linear-structured light under high-precision industrial measurement situations. We propose a kind of light plane calibration method that improves accuracy using line-structured light and stereo vision. It has more matching points and higher location accuracy. Firstly, we extract the laser points from stereo image pairs accurately with the designed target after intrinsic parameters calibrated by the Tsai method. Secondly, we match the laser points and reconstruct them in 3d space to obtain the 3d laser point. Repeat the point obtain process in different positions until the 3d laser point set consists of enough points. Then, we construct an optimization problem with fuzzy membership grades expert weight. Finally, the light plane is calibrated by solving the optimization problem with the least square method. The algorithm's availability is assessed by applying a self-referenced line-structured light scanning system with two cameras. Experimental results indicate that the RMS calibration error with the proposed algorithm is less than 0.033 mm, and the scanning error is 0.064 mm. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm capable of modelling shadows from nearby obstructions onto photovoltaic arrays is proposed. The algorithm developed is based on the calculation of the solar position in the sky for any given instant in order to obtain the shadow projection for any object point. The computation is based on considering the shadows as convex regions and on a rasterization process to evaluate the shadowed area of the array. The idea is extended to provide the shading patterns for a desired range of time and to calculate the efficiency rate of the irradiation power incident on the array in comparison with the non-shadowed case. The algorithm has interesting applications, such as optimizing array positioning and orientation, evaluating the impact of new obstructions on pre-existing array installations, allowing precise and practical data for control strategies and MPPT techniques for partially shaded systems, calculating more realistically constrained payback scenarios and finding the optimal PV array interconnection. The results are illustrated by three numerical examples, in which the effects of a nearby building in the irradiation received by a photovoltaic array throughout the year, panel relocation and different interconnections are analysed. [ABSTRACT FROM AUTHOR]

To classify sea targets of underwater and surface groups. A novel hybrid classification algorithm based on sonar, automatic identification system (AIS) and radar is proposed in this paper. The proposed method includes four parts. The data preprocessing, the multi-target data association, the multi-sensor multi-target correlation, and the underwater/surface probability distribution fusion. Firstly, the measurement data of multiple sensors are unified in time and space through space-time registration. Secondly, the measurement data of each sensor are separated into different target sets by Mahalanobis distance discriminant method. And each target is modeled by grey prediction GM (1,1) model subsequently, and the noise of data are filtered by Kalman filter (KF). Thirdly, it preliminarily determines the type of targets by Hungarian algorithm. Finally, the D–S evidence theory based on the Angle cosine and Lance distance (ALDS) is used to further determines the target type. The proposed methods can be applied when there is inconsistent evidence. Simulation results illustrate that the proposed algorithm is effective in decision support for sea target classification. [ABSTRACT FROM AUTHOR]

This paper presents a method for improving indices and costs associated with reliability in radial distribution systems through switch placement using genetic algorithms. On this paper, the switch allocation problem will be solved with a method that allows novelties that can improve the quality of the solutions obtained. First, the possibility of switch allocation on both sides of each section of a feeder is proposed. Second, a method to quantify the non-supplied energy in terms of dollars per kWh of interruption is expanded, allowing a versatile way to simulate cases in which individual customers have different economic impacts when in situation of interrupted power delivery. The proposed algorithm is also versatile when additional restrictions are applied, such as fixing the number of switches or imposing SAIDI limits. Optimal switch locations are defined to improve restoration and reduce costs associated with reliability. Simulations in test and real feeders are performed, and the presented results show significant improvements. [ABSTRACT FROM AUTHOR]

Transshipment problems are special type of transportation problems in which goods are transported from a source to a destination through various intermediate nodes (sources/destinations), possibly to change the modes of transportation or consolidation of smaller shipments into larger or deconsolidation of shipments. These problems have found great applications in the era of e-commerce. The formulation of transshipment problems involves knowledge of parameters like demand, available supply, related cost, time, warehouse space, budget, etc. However, several types of uncertainties are encountered in formulating transshipment problem mathematically due to factors like lack of exact information, hesitation in defining parameters, unobtainable information or whether conditions. These types of uncertainty can be handled amicably by representing the related parameters as intuitionistic fuzzy numbers. In this article, a fully fuzzy transshipment problem is considered in which the related parameters (supply, demand and cost) are assumed to be represented as trapezoidal intuitionistic fuzzy numbers. The proposed method is based on ambiguity and vagueness indices, thereby taking into account hesitation margin in defining the values precisely. These indices are then used to rank fuzzy numbers to derive a fuzzy optimal solution. The technique described in this paper has an edge as it directly produces a fuzzy optimal solution without finding an initial basic feasible solution. The method can easily be employed to fuzzy transshipment problems involving trapezoidal intuitionistic, triangular intuitionistic, trapezoidal, triangular, interval valued fuzzy numbers and real numbers. The proposed technique is supported by numerical illustrations and it has been shown that the method described in the paper is computationally much more efficient than already existing method and is applicable to a larger set of problems. [ABSTRACT FROM AUTHOR]

Unified power quality conditioner (UPQC) is exploited to alleviate the issues associated with voltage swell/dip in source voltage, and it regulates the load voltage absolutely. It is deployed to resolve the entire issues associated with current and voltage harmonics and enhance power quality. In order to solve those issues, optimal sizing and location of UPQC are adopted by the researchers, and it is still in progress. However, the power quality issues like power loss, UPQC cost and voltage stability index (VSI) are not analysed and considered. Hence, the tactics used in this paper help to design an optimal location and sizing of the UPQC in power system using a nonlinear multi-objective function. The objective function considers the minimization of power loss, UPQC cost and VSI. For attaining this objective, this paper hybridizes two well-performed optimization algorithms called lion algorithm (LA) and crow search algorithm (CSA). Since the mating of LA is carried out on the basis of CSA update, the proposed algorithm is termed as crow search mating-based lion algorithm (CSM-LA). The current experiment on localizing and sizing of UPQC is carried out in IEEE 33 and IEEE 69 benchmark test bus systems. The performance of the proposed model is distinguished with conventional methods in terms of performance and convergence analysis, and the relevant outcomes are attained which proves the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient maximum power point tracking control algorithm based on the Piyavskii–Shubert algorithm under partial shading conditions. The Piyavskii–Shubert algorithm, a deterministic global optimization algorithm, maximizes a function satisfying the Lipschitz continuity over a closed set. However, the algorithm often converges slowly because it uses an inefficient parameter even in neighborhoods of a global maximum. The proposed method accelerates convergence to the global maximum by adaptively changing the parameter based on prior information. Simulations are conducted to illustrate the effectiveness of the proposed method by comparing it with traditional methods. [ABSTRACT FROM AUTHOR]

According to the alpha-planes expression theory of general type-2 fuzzy sets, this paper completes the center-of-sets (COS) type reduction and defuzzification for general type-2 fuzzy logic systems (GT2 FLSs). In fact, it still remains an open problem by comparing the prevalent Karnik–Mendel (KM) algorithms and other types of alternative noniterative algorithms. The modules of fuzzy inference, COS type reduction, and defuzzification of Mamdani-type GT2 FLSs on the basis of Nagar-Bardini algorithms, Nie-Tan algorithms, and Begian-Melek-Mendel algorithms are also provided. Six simulation examples are provided to illustrate the performances of corresponding noniterative algorithms. In contrast to the KM algorithms, the suggested three types of noniterative algorithms can get faster convergence speeds. [ABSTRACT FROM AUTHOR]

The uncertainty associated with the financial domain in modern portfolio selection problems can be overcome by using fuzzy set theory. The portfolio is modified in this paper using the possibility theory instead of the probability theory by formulating the risk return as fuzzy numbers, and we also take into account the V-shaped transaction costs. The possibilistic semi-absolute deviation portfolio selection technique is used to develop a portfolio selection framework by considering the investor demands and stock characteristics. The higher computational complexity associated with the possibilistic mean semi-absolute deviation portfolio model is reduced using the hybris salp swarm-based Coot algorithm (SCOOT). The main aim of the hybrid SCOOT algorithm is to reduce the risk and increase the expected return. The salp swarm algorithm is integrated with the coot algorithm to enhance the global search capability. The performance of the proposed approach is evaluated with the extensive experiments conducted on the Bombay Stock Exchange dataset. The results obtained show that the proposed methodology offers better performance when considering the transaction costs, and its performance is very high when compared to the conventional metaheuristic techniques. [ABSTRACT FROM AUTHOR]

In order to control the systematic divergence among decision makers (DMs) and preserve the original decision preference, this paper proposes a novel decision information fusion framework under the hesitant fuzzy environment. First, a maximum compactness-based normalization method is presented to normalize hesitant fuzzy elements (HFEs) as pretreatment of decision data. Second, prospect theory is introduced to assign the optimal aggregation weights to maximize the efficiency of the preference aggregation process, in which the expected consensus threshold is viewed as a reference point estimated through statistic inference to distinguish DMs' status. Third, an effective feedback mechanism is designed to improve group consensus, and the dichotomy algorithm is utilized to search optimal feedback weight to preserve original decision information. Finally, a case study and comparison analysis are illustrated to show the efficiency of the proposed hesitant fuzzy information fusion method. [ABSTRACT FROM AUTHOR]

The set covering problem is a fundamental model which comprises a wide range of important applications such as crew scheduling problems that need to cover a set of trips. It is one of the most common issues in the facility location problem, which requires further investigations, particularly in emergency and service facilities. As such, the objective of this study is to propose a new coefficient-based heuristic algorithm for the set covering problems. This paper has accordingly presented the algorithm that evaluates the qualification of subsets by directly applying a fitness function. This fitness function is formulated based on sets and subsets coefficients in a way that the subsets of selected sets have the lowest probability to be selected in the next iteration. The algorithm is not only capable of constructing an answer within polynomial time, but can solve complex set covering problems without conventional restrictions. The performance of this algorithm is evaluated on benchmark instances including a set of reproduced and selected OR-library problems within different sizes. Computational results indicate that the proposed heuristic algorithm produces better solutions, especially in large-scale problems comparing simulated annealing in terms of quality and time. [ABSTRACT FROM AUTHOR]

With the deepening of the research on clustering algorithm, clustering validity has become an indispensable part of cluster analysis. However, due to the complexity of data structure and different attributes, any clustering validity function cannot be applied to all datasets, so clustering validity function has been bringing forth new ones. Therefore, this paper proposes a clustering validity function fusion model based on D–S evidence theory (DS-CVFFM), which adopts FCM clustering algorithm as the base algorithm, calculates the values of different validity functions, and then uses the values of different clustering validity functions as the evidence source to construct the basic probability assignment function (BPA). Finally, it integrates with the fusion rules of D–S evidence theory, and outputs the optimal clustering number according to the decision conditions. DS-CVFFM uses the information fusion of multiple clustering validity functions to judge the number of optimal clusters without the need to propose complex validity functions, and avoid the influence of expert factors in the weighted combination clustering validity evaluation method. Finally, 4 sets of artificial datasets and 14 sets of UCI datasets are selected to verify the effectiveness of the proposed model. The experimental results show that compared with the traditional clustering validity evaluation methods, the proposed fusion model has a significant improvement in the accuracy of judging the optimal number of clusters, and the stability is improved under different values of fuzzy exponent, which can overcome the shortcomings of traditional clustering validity evaluation methods. [ABSTRACT FROM AUTHOR]

Since the α-planes expression theory of general type-2 fuzzy sets (GT2 FSs) was put forward, the computational complexity of general type-2 fuzzy logic systems (GT2 FLSs) has been greatly reduced. Calculating the centroids of GT2 FSs is an important block for theoretical research of GT2 FLSs. Noniterative algorithms can overcome the disadvantages of being computationally intensive and time consuming. The paper discovers the relations between discrete types of noniterative algorithms and continuous types of noniterative algorithms. In terms of the Newton–Cotes quadrature formulas, three types of weighted-based noniterative algorithms are proposed to compute the centroids. In case of choosing the same sampling rate of primary variable, four computer simulation instances illustrate that, the proposed weighted-based noniterative algorithms have higher calculation accuracies and faster convergence speeds in contrast to the original noniterative algorithms. [ABSTRACT FROM AUTHOR]

This paper develops a general output error identification approach of nonlinear synchronous generator parameters. The considered method is based on a meta-heuristic optimization algorithm called stochastic fractal search (SFS). It consists of minimizing a quadratic criterion that represents the squared difference between the simulated model outputs and those obtained from the model to be identified by using the SFS algorithm. To highlight the performance of the proposed method, a deep comparison with the PSO algorithm was carried out. The obtained results proved the superiority of the proposed approach in terms of stability, robustness, estimation accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

This paper is concerned with fault estimation in a class of nonlinear fractional-order systems using a new second-order sliding mode observer. Since the existing sliding mode observers are troubled with the chattering phenomenon, a new observer structure is proposed, and finite-time convergence of error dynamics is proved using fractional-order super-twisting algorithm (FSTA). Two numerical examples of chaotic fractional-order systems and comparison with a similar observer show the chattering reduction and justify the proposed observer's effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a nonparametric identification technique to estimate second-order plus time delay (SOPTD) processes from step response is proposed, named NMIE. It is based on optimization procedures by using the Nelder-Mead algorithm. Two cases are presented to perform the identification procedure. First, the SOPTD models are estimated using simulated data of a generic transfer function and then of a shell and tube heat exchanger. Second, the variables temperature, pH and level are estimated, using real data of a pH neutralization plant. The Integral Equation method (IE) is used to compare its results with the NMIE method. The results show that the NMIE algorithm estimated better SOPTD parameters with greater fidelity than the IE method when different SNRs and sampling times are used. [ABSTRACT FROM AUTHOR]

A methodology for the synthesis of mixed H 2 / H ∞ controllers to stabilize delayed sampled-data LPV continuous-time systems is proposed in this paper. The technique consists of the resolution of a set of convex conditions for each considered norm, resulting in an appropriate gain-scheduling state-feedback gain guaranteeing the desired robustness and performance properties. The conditions are based on the application of Lyapunov–Krasovskii functionals, and an iterative algorithm is also proposed to enhance the norms minimization, thus reducing the conservatism of the technique. Numerical examples illustrate the validity of the method when compared to a similar technique in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical structure for decentralized power system state estimation whose lower level is conducted at each power network substation. Local level real-time modeling is based on a nonlinear state & topology coestimation algorithm which not only provides the nodal state variables, but also determines the correct substation topology. The higher hierarchical level, performed at the regional control center, coordinates the local estimates by processing power flow measurements taken on the network branches that interconnect the substations, followed by a second estimation stage that adds the contribution of synchrophasor measurements provided by phasor measurement units deployed in the network. In addition to other benefits, the proposed decentralized architecture allows a substantial reduction of the amount of data transmitted from substations to control centers, and also demands less computational effort than conventional centralized schemes. A number of case studies performed on an IEEE test system whose substations are represented at bus-section level are employed to evaluate the performance of the proposed state estimation architecture. [ABSTRACT FROM AUTHOR]

Electrical power system has to operate properly even in the presence of load variations and other disturbances. One of the concepts that define this condition is frequency stability, and to deal with this issue, load frequency controllers (LFCs) have to be well designed and parameterized. The most frequent control approaches for LFC are PID controllers, with automatic tuning strategies. Recently, fractional-order PID has gained much interest due to its ability to improve closed-loop performance. This paper presents a new hybrid load frequency control strategy, called AFOPID. The main property of this algorithm is, based on a FOPID, to combine fuzzy sets and differential evolution (DE) for optimal parameter settings. The AFOPID's parameters are tuned online in such a way that, in the occurrence of a load disturbance, the fuzzy logic updates its k p , k i , and k d coefficients to adapt the closed loop to the new operating condition. Then, the fractional coefficients λ and μ are updated by using a DE strategy. The hybrid system improves the overall solution since the fuzzy system first sets a good operation point and then the DE refines the solutions acting on the fractional orders. The proposed controller and tuning method are validated on a mathematical model of a hydroelectric plant belonging to the Brazilian National Interconnected System (SIN). The strategy has been compared with other similar algorithms in the same situation presenting better closed-loop performance. For controllers evaluating, some performance indices were used, such as the analysis of overshoots/undershoots, settling times, the integral square error, the integral time square error, and the integral time absolute error. The results have shown that the hybrid method has proved to provide better closed-loop performance than similar solutions or when each method is used alone. [ABSTRACT FROM AUTHOR]

In this paper, an online adaptive dynamic programming (OADP) combining with a disturbance observer is proposed to solve the problem of robust optimization for nonlinear systems. The scheme has only one neural network so it is more effective and simpler than others works which use two or three neural networks (two networks for action decision and one for disturbance estimation). Moreover, the stability of the overall system which includes the Actor, Critic, and disturbance observer components is mathematically proven through Lyapunov theory. Finally, the simulation and comparison are done to evaluate the correction and the advantages of the proposed algorithm. The simulation results show that the observer-based OADP technique has the ability to give the good response for the Planar robot even under conditions of system uncertainties and external disturbances. Moreover, they also demonstrate that proposed method is not only simpler but also more effective than other works, i.e., faster convergence, smaller error, and smaller storage requirement. [ABSTRACT FROM AUTHOR]

The paper aims to design and analyse Active Noise Control (ANC) performance in PVC Duct experimentally for reducing periodic background noises. In the ANC system, different adaptive methods are preferred to reduce the unwanted noise, of which the most preferred one is the famous Filtered cross Least Mean Square (F×LMS) algorithm. On the other hand, since it is structured with acoustic feedback issues, the ANC's efficacy is degraded and becomes unstable. This problem could be overcome by implementing a Feedback Neutralization (FN) concept in the ANC system based on the F×LMS algorithm's step-size parameter to control the unwanted noise. The small step-size with the ANC system is robust compared to the large step-size because the large size is susceptible to any random noise change. In this article, a Harmonic Mean dependent Variable Step-Size (HMVSS) method is proposed and developed in the feedback neutralization F×LMS algorithm to continuously change the algorithm's step-size corresponding to the reference noise and error signals from the sensors. The proposed method improves the convergence rate of the ANC method toward the desired response. The simulation results demonstrated that the proposed AHMVSS method could achieve better noise reduction and convergence speed compared to F×LMS, FN F×LMS algorithms. Besides, ANC is implemented in the PVC Duct noise control application using the proposed F×LMS feedback neutralization algorithm to produce a real-time anti-noise signal experimentally to limit PVC Duct noise. The experimental findings show that reducing noise in PVC Duct holds good, approximately 23–29 dB for different noises. [ABSTRACT FROM AUTHOR]

Service quality evaluation is of highly significance because it is the first step to make continuous improvements in providing service. However, the existing evaluation methods mostly model evaluation information by crisp number or Type-1 fuzzy set (T1 FSs), which cannot effectively reflect the uncertainty of users' perception. In this paper, a multi-attribute evaluation model based on interval type-2 fuzzy sets(IT2 FSs) is constructed and applied to service quality evaluation. First, an area similarity measure algorithm is proposed to calculate the similarity between two trapezoidal IT2 FSs. With the area similarity measure, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is modified to act as the evaluation approach. The evaluation model is then applied to a public transport service evaluation problem to sort each evaluation dimension to the predefined classes. The comparative analysis shows that our model can give more separated classifying results, which means a larger amount of information can be provided to decision-makers. [ABSTRACT FROM AUTHOR]

For most FCM-based fuzzy clustering algorithms, several problems, such as noise, non-spherical clusters, and size-imbalanced clusters, are difficult to solve. Different fuzzy clustering algorithms are developed to deal with these problems from different perspectives. However, no comprehensive viewpoint to generalize these problems has been put forward. In this paper, we reveal the inherent deficiency of FCM and propose a new fuzzy clustering method called Gaussian Collaborative Fuzzy C-means (GCFCM) to solve these problems. In GCFCM, Gaussian mixture model (GMM) and collaborative technology are adopted to enhance the ability of recognizing the intrinsic structure of clusters. Experimental results confirm that GCFCM is effective in dealing with noise, non-spherical clusters, size-imbalanced clusters, and those also show excellent performance in dealing with real-world data sets. [ABSTRACT FROM AUTHOR]

This paper proposes a fast adaptive parameter estimation method for maximum torque per ampere control (MTPA) of interior permanent magnet synchronous motor (IPMSM) drives. The adaptive features that enable fast parameter estimation are investigated, and an adaptive observer with a structure capable of achieving fast estimation is proposed. A cascade adaptive observer design strategy is presented in order to obtain the benefit of fast estimation from the proposed adaptive scheme. The MTPA is achieved through the solution of the stator current optimization problem combined with the IPMSM estimated parameters. Simulation and experimental results are provided in order to validate the proposed fast estimation adaptive MTPA algorithm. [ABSTRACT FROM AUTHOR]

Accurate phasor estimation under various faulty conditions is one of the most critical issues in the monitoring, protecting, and controlling of a microgrid (MG). This paper presents a new phasor estimation algorithm (PEA) under fault conditions for MG applications. The main features of this method are to accurately phasor estimation of the signal combination of a decaying dc offset, a decaying fundamental frequency component, and harmonics. In this strategy, a Taylor series is replaced by the exponential functions of the fundament frequency and decaying dc offset component. Then, the time constants and the magnitudes of decaying components are estimated by the least square method. The proposed method is explained theoretically, and its performance is demonstrated by simulating various numerical signals in MATLAB and measured signals during fault conditions. The simulation results indicate that the proposed method estimates the dynamic phasor during fault conditions, accurately. Furthermore, a comprehensive comparison with the conventional methods is carried out to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Omnidirectional mobile platform is essential due to its excellent mobility and versatility. With the development of the manufacturing industry, how to transport oversized or overweight goods has become a new problem. Compared with manufacturing omnidirectional mobile platforms with different specifications, it is more cost-effective and flexible to coordinate two non-physically connected omnidirectional platforms to transport overweight and oversized cargo. The roughness of the actual deployment environment and the mechanical deflection between the two vehicles have a significant impact on the normal operation of the system. This paper combines mechanical wheels, image processing algorithms and collaboration algorithms to create a novel and practical split-type omnidirectional mobile platform based on image deviation prediction for transporting oversized or overweighted goods. The proposed system collects raw measurements from a distance sensor and an image sensor, transmits them to a central processing unit through a wireless communication module and calculates and predicts the relative deflection between the two vehicles based on our derived mathematical model. This information is then fed to a Kalman filter and PID control algorithm to coordinate the two vehicles. The effectiveness and performance of our system have been thoroughly tested, which has already been applied in a bullet train production line. [ABSTRACT FROM AUTHOR]

System design deals with various challenges of targets and resources, such as reliability, availability, maintainability, cost, weight, volume, and configuration. This paper deals with the multi-objective system availability and cost optimization of parallel–series systems by resorting to the multi-objective strawberry algorithm also known as the Plant Propagation Algorithm or PPA and a fuzzy method. It is the first implementation of this optimization algorithm in the literature for this kind of problem to generate the Pareto Front. The fuzzy method allows helping the decision maker to select the best compromise solution. A numerical case study involving 10 subsystems highlights the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Research shows that though some students can easily find the correct solutions to the problem(s) at hand, the algorithms that these students create are not always ones that would, when implemented, produce the correct solution(s). Towards shedding light on this phenomenon, the present study explicates a mechanism—accounting for features of the solution—by which students create algorithms. Via this mechanism, students notice particular features of the solution they found, then create specific rules (instructions) within their algorithm, which guarantee that the algorithm outputs an object that possesses the noticed features. Thus, an algorithm can produce the solution only if the object it outputs has all the features of the solution. In this paper, I explicate the accounting for mechanism within the collaborative work of two groups of students on a contextualised graph theory task, which invited the groups to create an algorithm for finding an optimal seating arrangement. Both groups found an optimal seating arrangement and seemingly employed the accounting for mechanism to create their respective algorithms. However, only one group's algorithm could actually produce the optimal arrangement. The questions explored in this study are: (1) What sorts of features of their solutions did the two groups account for? and (2) What differences (if any) between the features accounted for by the respective groups, might explain why only one group's algorithm could produce the optimal arrangement? [ABSTRACT FROM AUTHOR]

Today's business environments are prone to high levels of uncertainties and risks because the speed of changes is too high. Hence, the way of dealing with threats has changed. Complex socio-technical systems (CSSs) are confronted with out-of-control disturbances that need risk management systems to be applied. Risk management is organized activities to monitor and control risks in CSSs. Newly, resilience engineering (RE) as a safety management discipline has been expanded to change the attitude to risks. RE is defined as the capability of a system to absorb disturbances and adapt to rapidly changing conditions. Process industries as CSS, are potentially disposed to catastrophic events such as explosions, toxic leakages, stoppages of production operations, etc. In this paper, a novel RE-based algorithm is developed to measure the efficiency of process industries for preventing and reducing disorders considering their high-level of uncertainty. To do this, firstly, resilience indices in an oil refinery as an essential process industry are extracted. Then, using type-2 fuzzy data envelopment analysis (DEA), a new multi-objective approach is presented to evaluate the efficiency of DMUs from the perspective of RE. Interval type-2 fuzzy sets (IT2 FSs) is used in this approach because they potentially hold more implication than the classical type-1 fuzzy sets (T1 FSs). As a case study, a refinery as a CSS is evaluated. Using the proposed algorithm shows the efficiency scores of DMUs in the refinery. By conducting a comparative analysis, it was revealed that the proposed model outperforms classical methods. The results of the proposed approach show that besides simplicity, there is good stability to consider the criteria of RE in efficiency measurement with IT2 FSs. [ABSTRACT FROM AUTHOR]

This paper focuses on the new model for the classification of railhead defects, through images acquired by a rail inspection vehicle. In this regard, we discuss the use of set-membership concept, derived from the adaptive filter theory, into the training procedure of an upper and lower singleton type-2 fuzzy logic system, aiming to reduce computational complexity and to increase the convergence speed. The performance is based on the data set composed of images provided by a Brazilian railway company, which covers the four possible railhead defects (cracking, flaking, head-check and spalling) and the normal condition of the railhead. Additionally, we apply different levels of additive white Gaussian noise in the images in order to challenge the proposed model. Finally, we discuss performance analysis in terms of convergence speed, computational complexity reduction, and classification ratio. The reported results show that the proposal achieved improved convergence speed, slightly higher classification ratio and remarkable computation complexity reduction when we limit the number of epochs for training, which may be required under real-time constraint or low computational resource availability. [ABSTRACT FROM AUTHOR]

Selecting right renewable energy resources (RESs) is emerging as a solution to alleviate energy crisis and environmental pollution. To better address the RESs selection problems, this paper proposes an enhanced Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS). Specifically, the decision information is characterized by linguistic terms and then encoded by interval type-2 fuzzy sets (IT2FSs). The current IT2FSs preprocessing models recklessly transform IT2FSs into crisp numbers, which may discount the superiority of applying fuzzy sets. Hence, we define a novel interval type-2 fuzzy projection model to measure the IT2FSs and some related theorems about the projection model are explored mathematically. Moreover, based on this new interval type-2 fuzzy projection model, an enhanced TOPSIS is proposed to calculate the closeness coefficients of alternatives. Of note, to keep information as much as possible, the obtained closeness coefficients are still IT2FSs. Finally, the Karnik–Mendel (KM) algorithms are employed to compare and rank those closeness coefficients. The effectiveness of the proposed method is demonstrated by a RESs selection case. Comparisons are also conducted to illustrate its advantages. [ABSTRACT FROM AUTHOR]