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Control technologies are of great importance in the rapidly developing electric vehicle industry. Control systems, especially in electric vehicles with more than one drive system or energy source, are at the top of the issues focused on to achieve low fuel and emission values, to provide adequate performance, and not to compromise on safety. Fuzzy logic controller (FLC) based systems are among the most preferred control systems with their adjustable, adaptable features, flexibility, and robust structure against uncertainty. Control systems developed using FLC are divided into sub-branches of propulsion, energy source, vehicle dynamic system, and environmental effects. In this review study, after revealing what needs to be done about the control system in these branches, it is explained why fuzzy logic is preferred in the solution of these problems and the studies done with FLC are summarized. It has been emphasized that fuzzy logic can be a leading solution tool in cases where the mathematical model cannot be presented. In addition, attention was drawn to optimization algorithms that provide the most accurate parameters to operate a fuzzy system in the most appropriate way, and examples of this were given. [ABSTRACT FROM AUTHOR]

We all wash our clothes on a daily basis. Traditionally, washing has been done by hand. However, today's technology has advanced greatly, and all manual labor has been replaced by machines. The washing machine is one such invention that allows consumers to conserve time, energy, and water. A fuzzy logic control system has been designed in response to human needs. A form of reasoning system known as fuzzy logic justifies YES or NO dependent on the input. These days, artificial intelligence is primarily used in automated products to mimic human thought. Many of the devices we use every day, including washing machines, air conditioners, satellites, unmanned aerial aircraft, traffic control systems, transmission systems, anti-lock brake systems, etc., utilize fuzzy logic problems. Python provides a straightforward answer to the fuzzy logic issue for the washing machine context. Until now, MATLAB was used to construct fuzzy logic problems for washing machines. However, Python logic is used in this, which mitigates the drawbacks of fuzzy logic in MATLAB. The type of clothing, level of grime, and load of clothing are the inputs for this procedure, and the wash time, RPM, dry time, and temperature are the outputs. This goal is used to cut down on the amount of time, energy, and water needed to wash clothes. The outcome of this simulation demonstrates that this washing machine offers a high-quality wash. [ABSTRACT FROM AUTHOR]

The inflation expectations of economic agents are one of the most important variables for central banks and monetary policy conduct, but such expectations are not directly observable. Consumers' expectations are examined in qualitative surveys. The key task is to transform consumers' survey responses into quantitative proxies of expectations. In this examination, we investigate an alternative method to quantify consumers' inflation expectations using fuzzy rule-based systems. In order to generate rules in the learning phase, we compare two methods: the Wang-Mendel method based on space partition and the subtractive clustering method. The learning data are information about past inflation and consumers' opinions as expressed in surveys. The system is built and tested on data from January 2002 to June 2019 for non-euro member states of the EU. The fuzzy rules-based system returns results that outperform a benchmark—the standard quantification procedure—in terms of correlation with a raw approximation of consumer expectations, which is survey balance statistics. The results are robust regarding the benchmark choice. We find the method more promising for economies with lower stability, as it gives more room for the training phase of the algorithm application. [ABSTRACT FROM AUTHOR]

This paper focuses on the limit cycles prediction problem to discuss the stability analysis of dynamic nonlinear interval type-2 Takagi–Sugeno–Kang fuzzy control systems (NIT2 TSK FCSs) with adjustable parameters. First, in order to alleviate computational burden, a simple architecture of NIT2 TSK FCS using two embedded nonlinear type-1 TSK fuzzy control systems (NT1 TSK FCSs) is proposed. Then, describing function (DF) of NIT2 TSK FCS is obtained based on the DFs of embedded NT1 TSK FCSs. Subsequently, integrating the stability equation and parameter plane approaches provides a solution to identify the limit cycle and the asymptotically stable regions. Moreover, particle swarm optimization technique is applied to minimize the limit cycle region. Furthermore, for robust design, a gain-phase margin tester is utilized to specify the minimum gain margin ( GM min ) and phase margin ( PM min ) when limit cycles can arise. Finally, two simulation examples are considered to validate the advantages of the presented method. [ABSTRACT FROM AUTHOR]

To improve today's complex systems composed of personnel, hardware, software and methods, it is necessary to identify, prioritize and select projects to eliminate the causes of underperformance. In this study, a methodology based on fuzzy analytical hierarchy process (FAHP) for decision-making, integrated with cause-and-effect diagrams used in quality improvement studies, is proposed for this purpose. It is assumed that the resources for improvement are scarce and the best use of these is needed. To guide practitioners, the methodology is illustrated with a real-world implementation for identification, prioritization and selection of improvement projects for a poor performing appointment system at a hospital. The cause-and-effect diagram is used to identify and organize the causes and their sub-causes leading to the poor performance, and to create a hierarchy of these by using the information obtained from experts, staff and patients. In order to prioritize the major and sub-causes as potential improvement project topics, FAHP methodology, which utilize human cognition and judgment power based on knowledge and experience, is applied and used for decision-making. The priorities corresponding to each major cause and sub-cause can be used to make a decision on improvement projects and their order due to scarce resources. The methodology is general and can be used in various application domains. [ABSTRACT FROM AUTHOR]

In this paper a robust hybrid observer-based hybrid adaptive fuzzy controller for large-scale multiple-input multiple-output (MIMO) systems with affine and nonaffine form in the presence of input saturation is presented. Based on an arbitrary control gain matrix, a hybrid control scheme is designed to avoid the possible singularity problem. The proposed control method is very simple, effective and robust. The states of the system and the mathematical model of the system under control do not need to be well known. An observer-based adaptive fuzzy system based on strictly positive real (SPR) theory is applied to approximate the system states, but it does not require SPR conditions to be known identified. A robust control term is utilized to compensate the both uncertainties and observer errors and attenuate the residual error to the desired level. Finally, to display the applicability and efficiency of the proposed control method, a MIMO affine system and a MIMO nonaffine system with input saturation are applied in simulations. [ABSTRACT FROM AUTHOR]

The hesitant fuzzy linguistic (HFL) variable can handle the uncertainty very well, and Muirhead mean (MM) operator can consider correlations among any amount of inputs by an alterable parameter, which is a generalization of some existing operators by changing the parameter values. However, the traditional MM is only suitable for crisp numbers. In this article, we enlarge the scope of the MM operator to the HFL circumstance, and two new aggregation operators are proposed, including the HFL Muirhead mean operator and the weighted HFL Muirhead mean (WHFLMM) operator. Simultaneously, we discuss some worthy characters and some special cases concerning diverse parameter values of these operators. Moreover, a multiple-attribute decision-making method under the HFL environment is developed based on the WHFLMM operator. Lastly, a numerical example is applied to explain the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) based on fuzzy theory have attracted widespread attentions in pattern recognition and machine learning. However, these SVMs have some limitation in dealing with some classification problems with fuzzy outputs, which results in the ignorance of the fuzziness of fuzzy outputs. Motivated by this, the possibility and necessity of fuzziness of fuzzy outputs are discussed, and the dynamic partitioning methods of these fuzzy output training samples are demonstrated based on credibility measure. Then, the corresponding dynamic credibility support vector machines based on fuzzy outputs are established, and the feasibility and effectiveness of credibility SVMs are shown by experimental results. [ABSTRACT FROM AUTHOR]

Set pair analysis (SPA) is an updated theory for dealing with the uncertainty, which overlaps with the other existing theories such as vague, fuzzy, intuitionistic fuzzy set (IFS). Keeping the advantages of it, in this paper, we propose some novel similarity measures to measure the relative strength of the different intuitionistic fuzzy sets (IFSs) after pointing out the weakness of the existing measures. For it, a connection number, the main component of SPA theory is formulated in the form of the degrees of identity, discrepancy, and contrary. Then, based on it some new similarity and weighted similarity measures between the connection number sets are defined. A comparative analysis of the proposed and existing measures are formulated in terms of the counter-intuitive cases for showing the validity of it. Finally, an illustrative example is provided to demonstrate it. [ABSTRACT FROM AUTHOR]

In the paper Crucial and unsolved problems on Atanassov’s intuitionistic fuzzy sets, D.-F. Li pointed out that some kind of definitions of operations over Atanassov’s intuitionistic fuzzy sets are incorrect. We can see that, near 30 years after the first Atanassov’s papers, there exist some misunderstandings related not only on the name, but also on the basic operations on IFSs. Those misunderstandings concern, this time, on the operations of the sum and product. Li also casts doubt the equivalence of the intuitionistic fuzzy sets and the interval-valued fuzzy sets. In this paper, the Li’s reasoning is presented and commented. [ABSTRACT FROM AUTHOR]

This paper deals with the simplest fuzzy PID controllers of the Takagi-Sugeno (TS) type. The term simplest is coined since minimal (two) number of fuzzy sets is used for fuzzification in these controllers. The mathematical models of these controllers are found using a modified rule base. The rule base consists of two rules which reduce the number of tuning parameters. Two classes of the simplest fuzzy PID controller are defined using algebraic product triangular norm and bounded sum/maximum triangular co-norm. It is shown that the fuzzy PID controller with modified TS rule base is equivalent to a nonlinear variable gain/ structure controller. The BIBO stability of the closed-loop control system is studied using the small gain theorem. The computational aspects of the controllers are investigated. The applicability of the simplest fuzzy PID controllers is demonstrated with the help of examples. [ABSTRACT FROM AUTHOR]

Control charts, used in many areas, are important for providing information about the status of the product control. Control charts allow us observation of abnormal conditions about a product and/or a process. These situations usually need to be interpreted by an expert. At this point, fuzzy numbers can be beneficial in reducing the differences between experts’ opinions and information loss. This is especially true for qualitative data, and for this reason, fuzzy numbers can be used to transform linguistic expressions into data. Although some recent studies have created control charts with fuzzy sets, most focused on type-1 fuzzy sets. Nevertheless, in real life, it may not always be possible to express these data as type-1 fuzzy sets; it may be more realistic to express some data as type-2 fuzzy sets. The purpose of this study is to create control charts using interval type-2 fuzzy numbers. Interval type-2 fuzzy control charts can be obtained by using different approaches, including defuzzification, centroid, type reduction and likelihood approaches. Comparisons are made between the interval type-2 fuzzy control charts and classical control charts. This study introduces likelihood method as a new approach to generate fuzzy control charts. The significant contribution of this paper to the relevant literature is that interval type-2 fuzzy set methods applied to different areas—such as likelihood, centroid, type reduction—are adapted to c-control charts for the first time. [ABSTRACT FROM AUTHOR]

Owing to the advancements in information and telecommunication technologies, mobile location-based services are able to use previously collected mobile check-in data. This multi-dimensional data can provide new opportunities for research problems such as establishing new platforms for location-based advertising and location-based personalized recommendations. In other words, location data of potential customers indicate personal interests and visiting preferences. In some cases, customers’ preferences could not be easily determined or predicted while considering visiting patterns of mobile users. In this respect, this study provides a novel retailer segmentation approach based on multi-criteria decision-making (MCDM) combined fuzzy data clustering. The proposed model consists of two phases: (1) an interval type 2 hesitant MCDM approach for the determination of location perceived value and (2) retailer (store) clustering via different product sale prices with fuzzy data-based fuzzy c means (FcM) clustering. Proposed approach enables the simplification of FcM clustering adaptation to non-symmetric fuzzy data using dissimilarity measure. Using this integrated approach, advertisers and recommender system suppliers will be able to manage their product-special offerings to customers considering retailer segments and shopping mall characteristics. Additionally, the proposed approach constitutes the infrastructure of location-based recommender systems under imprecise environment. [ABSTRACT FROM AUTHOR]

Exudates are a common complication of diabetic retinopathy and the leading cause of blindness in the developing countries, especially in Thailand. The digital retinal images are usually interpreted visually by an expert ophthalmologist in order to diagnose exudates. However, detecting exudates in a large number of the digital retinal images is mostly manual and very expensive in expert ophthalmologist time and subject to human errors. In this research, we propose a novel retinal image analysis for detecting exudates through image preprocessing methods, i.e., histogram matching, local contrast enhancement, median filter, color space selection, and optic disc localization. Our in-depth retinal analysis indicates that the overall image quality is sensitive to the quality score. In the detection process, the exudates are detected by using naïve Bayesian classifier, support vector machine, and fuzzy C-means clustering method. Afterward, the exudates extracted from fuzzy C-means clustering are used as input to the mathematical morphology to obtain the final exudates detection quality score. Additionally, the optimal parameters of the mathematical morphology will increase the accuracy of the results from merely fuzzy C-means clustering method by 12.05%. The combination of these methods demonstrated an overall pixel-based accuracy of 97.45% including 97.12% sensitivity and 97.89% specificity. [ABSTRACT FROM AUTHOR]

The notion of fuzzy extended filters is introduced on residuated lattices, and its essential properties are investigated. By defining an operator ⇝ between two arbitrary fuzzy filters in terms of fuzzy extended filters, two results are immediately obtained. We show that (1) the class of all fuzzy filters on a residuated lattice forms a complete Heyting algebra, and its classical version is equivalent to the one introduced in Kondo (Soft Comput 18(3):427-432, 2014), which is defined with respect to (crisp) generated filters of singleton sets; (2) the connection between fuzzy extended filters and fuzzy generated filters is built, with which three other classes generating complete Heyting algebras, respectively, are presented. Finally, by the aid of fuzzy t-filters, we also develop the characterization theorems of the special algebras and quotient algebras via fuzzy extended filters. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective maximum power point tracking (MPPT) control for photovoltaic (PV) system to guarantee both H2 optimal control and H∞ model reference tracking performance simultaneously. First, the Takagi and Sugeno (T-S) fuzzy model is employed to describe the dynamic behavior of DC-DC boost converter. Then, based on this exact T-S fuzzy representation, multiobjective H2/H∞ MPPT controller is designed to minimize concurrently the H2 tracking error and H∞ disturbance attenuation level for the PV system. A specified T-S reference model is constructed to provide the desired trajectory which must be tracked. An MPP searching algorithm is also added in the global MPPT structure to generate the optimal PV current which is considered as input control for the T-S reference model. Finally, simulation results are given to illustrate the optimal tracking performance of the proposed fuzzy controller even when rapidly changing climatic conditions. [ABSTRACT FROM AUTHOR]

In the attribute-based encryption (ABE) systems, users could encrypt and decrypt messages based on some attributes or access policies. Due to the functionality and flexibility of ABE, it is considered to be very suitable for secure data sharing in cloud storage environment. However, in the real world, users’ access rights are often dynamic; therefore, we need ABE schemes to support revocation to meet this requirement. In this work, we construct a novel directly revocable ciphertext-policy ABE (DR-CP-ABE) scheme based on the multilinear maps and prove its selective security under (d+3)-multilinear decisional Diffie-Hellman assumption in the random oracle model. In addition, we extend our DR-CP-ABE scheme to support verifiable ciphertext delegation property. [ABSTRACT FROM AUTHOR]

This paper presents a novel Fuzzy-based bending angle predictor in laser bending process. Upon the case, and situations, different input data, membership functions and rules are developed dynamically for the Fuzzy predictor. Our main focus in developing the proposed controller is keeping generality of design. So, the controller can be adapted in different cases easily. To compensate for the possible lack of knowledge of experts for developing rule base of a Fuzzy controller, here, nonlinear regression is used as an alternative approach for developing the rule base. Furthermore, the performance of the controller is improved using particle swarm optimization (PSO) method. Also, based on the proposed Fuzzy controller and PSO, another predictor able to find one possible set of input values to catch a predefined angle is proposed. Several experimental tests were conducted to evaluate performance of the proposed controllers. Comparing experimental and predicted results shows that they are in a proper agreement with our claim. [ABSTRACT FROM AUTHOR]

Kripke frames (and models) provide a suitable semantics for sub-classical logics; for example, intuitionistic logic (of Brouwer and Heyting) axiomatizes the reflexive and transitive Kripke frames (with persistent satisfaction relations), and the basic logic (of Visser) axiomatizes transitive Kripke frames (with persistent satisfaction relations). Here, we investigate whether Kripke frames/models could provide a semantics for fuzzy logics. For each axiom of the basic fuzzy logic, necessary and sufficient conditions are sought for Kripke frames/models which satisfy them. It turns out that the only fuzzy logics (logics containing the basic fuzzy logic) which are sound and complete with respect to a class of Kripke frames/models are the extensions of the Gödel logic (or the super-intuitionistic logic of Dummett); indeed this logic is sound and strongly complete with respect to reflexive, transitive and connected (linear) Kripke frames (with persistent satisfaction relations). This provides a semantic characterization for the Gödel logic among (propositional) fuzzy logics. [ABSTRACT FROM AUTHOR]

This paper presents a new fuzzy bee colony optimization method to find the optimal distribution of the membership functions in the design of fuzzy controllers for complex nonlinear plants. We used interval type-2 and type-1 fuzzy logic systems in dynamically adapting the alpha and beta parameter values of the bee colony optimization algorithm (BCO). Simulation results with a type-1 fuzzy logic controller for benchmark control plants are presented. The advantage of using interval type-2 fuzzy logic systems for dynamic adjustment of parameters in BCO applied in fuzzy controller design is verified with two benchmark problems. We considered different levels and types of noise in the simulations to analyze the approach of interval type-2 fuzzy logic systems to find the best values of alpha and beta for BCO when applied in the design of fuzzy controllers. [ABSTRACT FROM AUTHOR]

For realizing the flexible, scalable and fuzzy fine-grained access control, ciphertext policy attribute-based encryption (CP-ABE) scheme has been widely used in the cloud storage system. However, the access structure of CP-ABE scheme is outsourced to the cloud storage server, resulting in the disclosure of access policy privacy. In addition, there are multiple authorities that coexist and each authority is able to issue attributes independently in the cloud storage system. However, existing CP-ABE schemes cannot be directly applied to data access control for multi-authority cloud storage system, due to the inefficiency for user revocation. In this paper, to cope with these challenges, we propose a decentralized multi-authority CP-ABE access control scheme, which is more practical for supporting the user revocation. In addition, this scheme can protect the data privacy and the access policy privacy with policy hidden in the cloud storage system. Here, the access policy that is realized by employing the linear secret sharing scheme. Finally, the security and performance analyses demonstrate that our scheme has high security in terms of access policy privacy and efficiency in terms of computational cost of user revocation. [ABSTRACT FROM AUTHOR]

In this paper, a set of novel fuzzy adaptive control strategy is proposed to control two totally different nonlinear systems with complicated structure and different numbers of nonlinear terms. This novel adaptive control strategy is composed of three main points-(1) novel fuzzy model, proposed via Li and Ge (IEEE Trans Syst Man Cybern Part B Cybern 41(4):1015-1026, 2011), provides a set of simple and effective modeling strategy to reduce the total numbers of fuzzy rules through modeling process, and only two linear subsystems are needed; (2) novel control Lyapunov function is set as an exponential form of error states and tries to speed up the variance of states in this article; and (3) pragmatical asymptotically stability theorem: this theorem can be applied to proof that the error of parameters can achieve the original point strictly. Mathieu-Van der Pol system and quantum cellular neural networks nanosystem are used for illustrations in numerical simulation results to show the effectiveness and feasibility of the new fuzzy adaptive approach. Traditional fuzzy modeling and adaptive control method are given for further comparison. [ABSTRACT FROM AUTHOR]

In this paper, design and performance analysis of bacterial foraging optimization algorithm (BFOA)-optimized fuzzy PI/PID (FPI/FPID) controller for automatic generation control of multi-area interconnected traditional/restructured electrical power systems is presented. Firstly a traditional two-area non-reheat thermal system is considered, and gains of the fuzzy controller are tuned employing BFOA using integral of squared error objective function. The supremacy of this controller is demonstrated by juxtaposing the results with particle swarm optimization (PSO), firefly algorithm (FA), BFOA, hybrid BFOA-PSO-based PI and fuzzy PI controllers based upon pattern search (PS) and PSO algorithms for the same power system structure. The approach is then extended to a two-area reheat system, and improved results are found with the purported FPI/FPID controller in comparison with PSO and artificial bee colony optimized PI controller. Further, the approach is implemented on a traditional multi-source multi-area (MSMA) hydrothermal system and its superb performance is observed over genetic algorithm and hybrid FA-PS tuned PI controller. Additionally, to demonstrate the scalability of the designed controller to cope with restructured power system, the study is also protracted to a restructured MSMA hydrothermal power system. Finally, sensitivity analysis is performed to ascertain the robustness of the controller designed for the systems under study. It is observed that the suggested FPI/FPID controller optimized for nominal conditions is able to handle generation rate constraints and wide variations in nominal loading condition as well as system parameters. [ABSTRACT FROM AUTHOR]

Classifying the pixels of satellite images into homogeneous regions is a very challenging task as different regions have different types of land covers. Some land covers contain more regions, while some contain relatively smaller regions (e.g., bridges, roads). In satellite image segmentation, no prior information is available about the number of clusters. Here, in this paper, we have solved this problem using the concepts of semi-supervised clustering which utilizes the property of unsupervised and supervised classification. Three cluster validity indices are utilized, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. The first two cluster validity indices, symmetry distance based Sym-index, and Euclidean distance based I-index, are based on unsupervised properties. The last one is a supervised information based cluster validity index, Minkowski index. For supervised information, initially fuzzy C-mean clustering technique is used. Thereafter, based on the highest membership values of the data points to their respective clusters, randomly 10 % data points with their class labels are chosen. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on three satellite image data sets of different cities of India. Results are also compared with existing clustering techniques. [ABSTRACT FROM AUTHOR]

The reliability of peers is very important for safe communication in peer-to-peer (P2P) systems. The reliability of a peer can be evaluated based on the reputation and interactions with other peers to provide different services. However, for deciding the peer reliability there are needed many parameters, which make the problem NP-hard. In this paper, we present two fuzzy-based systems (called FBRS1 and FBRS2) to improve the reliability of JXTA-overlay P2P platform. In FBRS1, we considered three input parameters: number of interactions (NI), security (S), packet loss (PL) to decide the peer reliability (PR). In FBRS2, we considered four input parameters: NI, S, PL and local score to decide the PR. We compare the proposed systems by computer simulations. Comparing the complexity of FBRS1 and FBRS2, the FBRS2 is more complex than FBRS1. However, it also considers the local score, which makes it more reliable than FBRS1. [ABSTRACT FROM AUTHOR]

Typical hesitant fuzzy sets (THFSs), possessing a finite-set-valued fuzzy membership degrees called typical hesitant fuzzy elements (THFEs), is a special kind of hesitant fuzzy sets. Fuzzy inclusion relationship, as the order structure in fuzzy mathematics, plays an elementary role in the theoretical research and practical applications of fuzzy sets. In this paper, a new partial order for THFEs is defined via the disjunctive semantic meaning of a set, based on which fuzzy inclusion relationship is defined for THFSs. Furthermore, inclusion measures are defined to present the quantitative ranking of every two THFEs and THFSs and different inclusion measures are constructed. The related similarity measure, distance and fuzzy entropy of THFSs are presented and their relationship with inclusion measures are investigated. Finally, an example is given to show that the inclusion measure can be applied effectively in hesitant fuzzy multi-attribute decision making. [ABSTRACT FROM AUTHOR]

Aiming at the weakness of the existing cloud neural network on training and practicality, a new improved structure of cloud neural network is designed. A hidden layer is added prior to the inverse cloud layer. Threshold level is set to zero and a simple training method is designed. In addition, considering the ignorance of signal randomness and fuzziness in the existing method of the flatness signal recognition, the cloud neural network combines the advantages of the fuzziness and randomness of cloud model and the learning and memory ability of neural network. Thus it is applied in the flatness signal recognition. The simulation contrast results demonstrate that the improved structure is able to identify common defects in shape with higher identity precision. [ABSTRACT FROM AUTHOR]

In recent years, Multi-Objective Evolutionary Algorithms ( moeas) that consider diversity as an objective have been used to tackle single-objective optimisation problems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand-tuning of algorithms, the use of automated parameter control approaches that are able to adapt parameter values during the course of an evolutionary run are becoming more common in the field of Evolutionary Computation ( ec). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parameter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self-adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark problems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a significant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea outperforms the single-objective scheme in most cases, thus showing the benefits of solving single-objective problems through diversity-based multi-objective schemes. [ABSTRACT FROM AUTHOR]

Most image compression algorithms suffer from several drawbacks: high-computational complexity, moderate reconstructed picture qualities, and a variable bit rate. In this paper, an efficient color image quantization technique that depends on an optimized Fuzzy C-means (OFCM) algorithm is proposed. It exploits the optimization capability of the improved artificial fish swarm algorithm to overcome the shortage of Fuzzy C-means algorithm. It uses error diffusion algorithms to obtain perceptually better images after quantization. Experiments are carried out to estimate the performance of the proposed OFCM algorithm in image compression using standard image set. The results indicate that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction. The performance efficiency of the proposed technique is compared with those of three other quantization algorithms. The Comparative results confirmed that the OFCM has potential in terms of both accuracy and perceptual quality as compared to recent methods of the literature. [ABSTRACT FROM AUTHOR]

In this paper, we study the use of various strategies for selecting the server node in Peer to Peer networks, which are especially oriented networks with limited resources such as wireless mesh networks based on WiFi technology. We examine three different strategies: Random, the currently most used one, in which the server node is randomly chosen, Min-Hop, that selects the path with the least number of hops, and Purely Fuzzy, where the selection is made from a fuzzy inference process using the number of hops and the ETX cost (Expected Transmission Count) as fuzzy inputs. We analyze two different transmission scenarios: without obstacles and with obstacles between nodes. Results show that the currently more extended random strategy is the least efficient in most of cases, Min-Hop and Purely Fuzzy have a very similar behavior in networks without obstacles, and Purely Fuzzy is clearly more efficient in networks with obstacles. The study of performance was carried out using a wireless network simulation tool for discrete event simulation OMNeT++. [ABSTRACT FROM AUTHOR]

In this paper, we present the application of neuro-fuzzy system-ANFIS for air conditioning systems to reduce electricity consumption. The current buildings have automation systems that provide data on lighting, electrical system, air conditioning system, etc. We studied the air conditioning system, in particular, to reduce energy consumption as air conditioning has a high consumption value. Our main goal in this study is the application of neuro-fuzzy system-ANFIS with the adjustment of the rules made by a decision tree-CART algorithm in air conditioning system. We compared the results of the application of the ANFIS-CART system with the application of PID controllers and fuzzy control system for a central air conditioning. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) networks, will be very important for future distributed systems and applications. In such networks, peers are heterogeneous in providing the services and they do not have the same competence of reliability. Therefore, it is necessary to estimate whether a peer is trustworthy or not for file sharing and other services. In this paper, we propose two fuzzy-based trustworthiness system for P2P communication in JXTA-overlay. System 1 has only one fuzzy logic controller (FLC) and uses four input parameters: mutually agreed behaviour ( MAB), actual behaviour criterion ( ABC), peer disconnections ( PD) and number of uploads ( NU) and the output is peer reliability ( PR). System 2 has two FLCs. In FLC1 use three input parameters: number of jobs ( NJ), number of connections ( NC) and connection lifetime ( CL) and the output is actual behavioural criterion ( ABC). We use ABC and reputation ( R) as input linguistic parameters for FLC2 and the output is peer reliability ( PR). We evaluate the proposed systems by computer simulations. The simulation results show that the proposed systems have a good behaviour and can be used successfully to evaluate the reliability of the new peer connected in JXTA-overlay. [ABSTRACT FROM AUTHOR]

The well known Takagi-Sugeno (T-S) fuzzy model can be extended in different ways including the polynomial fuzzy model, whose consequent parts are polynomial sub-systems. Compared with the traditional T-S fuzzy model, the polynomial fuzzy model can represent a nonlinear system more accurately with a smaller number of fuzzy logic rules. It is worth emphasizing that the stability analysis and controller design of polynomial fuzzy model-based (PFMB) control systems are not based on the linear matrix inequalities but the recently developed sum-of-squares decompositions. In this paper, based on an existing result for traditional fuzzy control systems, we propose a new stability condition for the stability analysis of PFMB control systems. Furthermore, the stability of PFMB control systems with parameter uncertainties is investigated. The popular inverted pendulum and an unstable nonlinear system are employed to demonstrate the quality of the proposed stability conditions. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to predict limit cycles of a dynamic fuzzy control system by combining a stability equation, describing function and parameter plane. The stability of a linearized dynamic fuzzy control system is then analyzed using stability equations and the parameter plane method, with the assistance of a describing function method. This procedure identifies the amplitude and frequency of limit cycles that are clearly formed by the dynamic fuzzy controller in the parameter plane. Moreover, the suppression of the limit cycle by adjusting control parameters is proposed. Continuous and sampled-data systems are addressed, and the proposed approach can easily be extended to a fuzzy control system with multiple nonlinearities. Simulations are performed to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Extensible Markup Language (XML) is a simple, flexible text format derived from SGML, which is originally designed to support large-scale electronic publishing. Nowadays XML plays a fundamental role in the exchange of a wide variety of data on the Web. As XML allows designers to create their own customized tags, enables the definition, transmission, validation, and interpretation of data between applications, devices and organizations, lots of works in soft computing employ XML to take control and responsibility for the information, such as fuzzy markup language, and accordingly there are lots of XML-based data or documents. However, most of mobile and interactive ubiquitous multimedia devices have restricted hardware such as CPU, memory, and display screen. So, it is essential to compress an XML document/element collection to a brief summary before it is delivered to the user according to his/her information need. Query-oriented XML text summarization aims to provide users a brief and readable substitution of the original retrieved documents/elements according to the user's query, which can relieve users' reading burden effectively. We propose a query-oriented XML summarization system QXMLSum, which extracts sentences and combines them as a summary based on three kinds of features: user's queries, the content of XML documents/elements, and the structure of XML documents/elements. Experiments on the IEEE-CS datasets used in Initiative for the Evaluation of XML Retrieval show that the query-oriented XML summary generated by QXMLSum is competitive. [ABSTRACT FROM AUTHOR]

When we face a problem with a high number of variables using a standard fuzzy system, the number of rules increases exponentially and the obtained fuzzy system is scarcely interpretable. This problem can be handled by arranging the inputs in hierarchical ways. This paper presents a multi-objective genetic algorithm that learns serial hierarchical fuzzy systems with the aim of coping with the curse of dimensionality. By means of an experimental study, we have observed that our algorithm obtains good results in interpretability and accuracy with problems in which the number of variables is relatively high. [ABSTRACT FROM AUTHOR]

Based on the equivalence relation on a linear space, in this paper we introduce the definition of rough convex cones and rough convex fuzzy cones and discuss some of the fundamental properties of such rough convex cones. [ABSTRACT FROM AUTHOR]

Dealing with high-dimensional data has always been a major problem with the research of pattern recognition and machine learning, and linear discriminant analysis (LDA) is one of the most popular methods for dimensionality reduction. However, it suffers from the problem of being too sensitive to outliers. Hence to solve this problem, fuzzy membership can be introduced to enhance the performance of algorithms by reducing the effects of outliers. In this paper, we analyze the existing fuzzy strategies and propose a new effective one based on Markov random walks. The new fuzzy strategy can maintain high consistency of local and global discriminative information and preserve statistical properties of dataset. In addition, based on the proposed fuzzy strategy, we then derive an efficient fuzzy LDA algorithm by incorporating the fuzzy membership into learning. Theoretical analysis and extensive simulations show the effectiveness of our algorithm. The presented results demonstrate that our proposed algorithm can achieve significantly improved results compared with other existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, on the basis of breadth-first and depth-first ways, we establish a fundamental framework of fuzzy grammars based on lattices, which provides a necessary tool for the analysis of fuzzy automata. The relationship among finite automata with membership values in lattices ( l-VFAs), lattice-valued regular grammars ( l-RGs) and lattice-valued deterministic regular grammars ( l-DRGs) is investigated. It is demonstrated that, based on each semantic way, l-VFAs and l-RGs are equivalent in the sense that they accept or generate the same classes of fuzzy languages. Furthermore, it is proved that l-VFAs, l-valued deterministic finite automata, l-RGs and l-DRGs are equivalent based on depth-first way. For any l-RG, the language based on breadth-first way coincides with the language based on depth-first way if and only if the truth-valued domain l is a distributive lattice. [ABSTRACT FROM AUTHOR]

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology. [ABSTRACT FROM AUTHOR]

Network intrusion detection systems (NIDSs) are pattern recognition problems that classify network traffic patterns as either 'normal' or 'abnormal'. Precisely, the main aim of intrusion detection is to identify unauthorized use, misuse, and abuse of computers by detecting malicious network activities such as port scans, denial of service or other attempts to crack computer network environments. Even though the incorporation of conventional Soft Computing techniques in NIDSs has yielded to good solutions, the strong dynamism characterizing network intrusion patterns tend to invalidate the usability of existing framework. To tackle this issue, our proposal performs an adaptive supervised learning on a collection of time series that characterizes the network behavior to create a so-called timed automata-based fuzzy controller (TAFC), i.e. an evolvable fuzzy controller whose dynamic features allow to design an advanced network intrusion detection system able to directly deal with computer network dynamism and support networks' administrators to prevent eventual damages coming from unauthorized network intrusion. As will be shown in experiments, where our approach has been compared with a conventional Mamdani fuzzy controller, the proposed system reduces the detection error and, as consequence, improves the computer network robustness. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including the application of the fuzzy control techniques, the improvement of vehicle detection in flow monitoring systems and the technique of combining search metaheuristics with clustering.