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Fuzzy Systems, IEEE Transactions on

Issue 6 • Date Dec. 2011

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  • Table of contents

    Publication Year: 2011 , Page(s): C1
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  • IEEE Transactions on Fuzzy Systems publication information

    Publication Year: 2011 , Page(s): C2
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  • Stability Analysis and Control of Discrete Type-1 and Type-2 TSK Fuzzy Systems: Part I. Stability Analysis

    Publication Year: 2011 , Page(s): 989 - 1000
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (651 KB) |  | HTML iconHTML  

    This paper introduces sufficient conditions for the exponential stability of type-1 and type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems. A major advantage of the new conditions is that they do not require the existence of a common Lyapunov function and are, therefore, applicable to systems with unstable consequents. In addition, our results include two classes of type-2 TSK systems with type-1 consequents for which no stability tests are available. The use of the conditions in stability testing is demonstrated using simple numerical examples that include cases where methods that are based on a common Lyapunov function fail. The application of the stability test to develop new controller design methodologies is presented in a separate paper (i.e., Part II). View full abstract»

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  • Stability Analysis and Control of Discrete Type-1 and Type-2 TSK Fuzzy Systems: Part II. Control Design

    Publication Year: 2011 , Page(s): 1001 - 1013
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1051 KB) |  | HTML iconHTML  

    This paper proposes a new control system design methodology for type-1 and type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems that are based on new stability conditions. The stability conditions are discussed in a companion paper (Part I) and are used in the proofs of our main results. A major advantage of the new methodology is that it does not require a common Lyapunov function and is therefore applicable to systems with nonstabilizable consequents. Our controllers include fuzzy type-1 proportional and proportional-integral (PI) controllers, as well as constant state feedback for the same systems. The controller results in an exponentially stable system, and the designer can specify the rate of exponential convergence. The controller designs can be tested by the usage of linear matrix inequalities (LMIs). The design methodology is demonstrated by the usage of simple examples where methods that are based on a common Lyapunov function fail and physical systems where the new methodology provides better performance. View full abstract»

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  • Asynchronous Output-Feedback Control of Networked Nonlinear Systems With Multiple Packet Dropouts: T–S Fuzzy Affine Model-Based Approach

    Publication Year: 2011 , Page(s): 1014 - 1030
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (729 KB) |  | HTML iconHTML  

    This paper investigates the problem of robust output-feedback control for a class of networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi-Sugeno (T-S) fuzzy affine dynamic models with norm-bounded uncertainties, and stochastic variables that satisfy the Bernoulli random binary distribution are adopted to characterize the data-missing phenomenon. The objective is to design an admissible output-feedback controller that guarantees the stochastic stability of the resulting closed-loop system with a prescribed disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the controller implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with an S-procedure and some matrix inequality convexifying techniques, two different approaches to robust output-feedback controller design are developed for the underlying T-S fuzzy affine systems with unreliable communication links. The solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches. View full abstract»

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  • Set Measure Directed Multi-Source Information Fusion

    Publication Year: 2011 , Page(s): 1031 - 1039
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (256 KB) |  | HTML iconHTML  

    Our concern here is with the multi-source fusion problem. Two important aspects of this problem are the representation of information provided by the sources and the formulation of the instructions on how to fuse the information provided, which we refer to as the fusion imperative. We investigate the use of a monotonic set measure as a means of representing the fusion imperative. We look at the fusion of various different types of information, precise data, uncertain information such as probabilistic and possibilistic. We also consider the case of imprecise uncertain information such as that represented by a Dempster-Shafer belief structure. View full abstract»

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  • On the Temporal Granularity in Fuzzy Cognitive Maps

    Publication Year: 2011 , Page(s): 1040 - 1057
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1913 KB) |  | HTML iconHTML  

    The theory of fuzzy cognitive maps (FCMs) is a powerful approach to modeling human knowledge that is based on causal reasoning. Taking advantage of fuzzy logic and cognitive map theories, FCMs enable system designers to model complex frameworks by defining degrees of causality between causal objects. They can be used to model and represent the behavior of simple and complex systems by capturing and emulating the human being to describe and present systems in terms of tolerance, imprecision, and granulation of information. However, FCMs lack the temporal concept that is crucial in many real-world applications, and they do not offer formal mechanisms to verify the behavior of systems being represented, which limit conventional FCMs in knowledge representation. In this paper, we present an extension to FCMs by exploiting a theory from formal languages, namely, the timed automata, which bridges the aforementioned inadequacies. Indeed, the theory of timed automata enables FCMs to effectively deal with a double-layered temporal granularity, extending the standard idea of B-time that characterizes the iterative nature of a cognitive inference engine and offering model checking techniques to test the cognitive and dynamic comportment of the framework being designed. View full abstract»

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  • Similarity-Based Inverse Approximate Reasoning

    Publication Year: 2011 , Page(s): 1058 - 1071
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (598 KB) |  | HTML iconHTML  

    This paper investigates the inverse problem of ap proximate reasoning [to conclude A' from B' and (A→ B)] from different angles. We develop a method to And a solution to the inverse problem by the use of the law of contrapositive symme try. In the process, generalized modus ponens, generalized modus tollens, and their generating functions are studied extensively. An attempt is made here to establish that the concept of similarity plays an important role in inverse approximate reasoning. We apply the concept of similarity to model inverse approximate reasoning and develop a new methodology that is named similarity-based inverse approximate reasoning for a solution to the problem. Algorithms are presented and illustrated with simple yet concrete examples. We then use Zadeh's compositional rule of inference (CRI) as a combi nation of cylindrical extension and projection and, thereby, model generalized modus tollens in a different way. We, thus, develop a third way to find a solution to the inverse problem of approximate reasoning. A comparison between the result of application of our technique and the real static characteristic of a dc shunt motor is presented. View full abstract»

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  • Possibilistic Minmax Regret Sequencing Problems With Fuzzy Parameters

    Publication Year: 2011 , Page(s): 1072 - 1082
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (472 KB) |  | HTML iconHTML  

    In this paper, a class of sequencing problems with uncertain parameters is discussed. The uncertainty is modeled by the usage of fuzzy intervals, whose membership functions are regarded as possibility distributions for the values of unknown parameters. It is shown how to use possibility theory to find robust solutions under fuzzy parameters; this paper presents a general framework, together with applications, to some classical sequencing problems. First, the interval sequencing problems with the minmax regret criterion are discussed. The state of the art in this area is recalled. Next, the fuzzy sequencing problems, in which the classical intervals are replaced with fuzzy ones, are investigated. A possibilistic interpretation of such problems, solution concepts, and algorithms for the computation of a solution are described. In particular, it is shown that every fuzzy problem can be efficiently solved if a polynomial algorithm for the corresponding interval problem with the minmax regret criterion is known. Some methods to deal with NP-hard problems are also proposed, and the efficiency of these methods is explored. View full abstract»

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  • Memristor Crossbar-Based Hardware Implementation of the IDS Method

    Publication Year: 2011 , Page(s): 1083 - 1096
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2727 KB) |  | HTML iconHTML  

    Ink drop spread (IDS) is the engine of an active learning method, which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system that is subjected to modeling. In spite of its excellent potential to solve problems such as classification and modeling compared with other soft-computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of the IDS method that is based on the memristor crossbar structure. In addition to simplicity, being completely real time, having low latency, and the ability to continue working properly after the occurrence of power failure are some of the advantages of our proposed circuit. Moreover, some of operations in the IDS method have fuzzy nature, and as we will show at the end of this paper, updation of rules in the IDS structure and spiky neural networks are very similar. Therefore, IDS can be considered as a new fuzzy implementation of artificial spiky neural networks as well. View full abstract»

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  • On the Equivalence Conditions of Fuzzy Inference Methods—Part 1: Basic Concept and Definition

    Publication Year: 2011 , Page(s): 1097 - 1106
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (973 KB) |  | HTML iconHTML  

    This paper addresses equivalence of fuzzy inference methods. It first presents several well-known fuzzy inference methods: the product-sum-gravity method, the simplified fuzzy inference method, the fuzzy singleton-type inference method, the single input rule modules connected type fuzzy inference method (SIRMs method), and the single input connected fuzzy inference method (SIC method). Second, three fuzzy inference methods of the product-sum-gravity method, simplified fuzzy inference method, and fuzzy singleton-type inference method, which are all widely used as fuzzy control methods, are shown to be equivalent to each other. Third, the equivalence conditions between the SIRMs method and the SIC method, known as single input type fuzzy inference method, are shown. Finally, it also gives the equivalence conditions between the single input type fuzzy inference methods and the previous three fuzzy inference methods. Investigating the equivalence among various fuzzy inference methods would help to understand the relationship of those fuzzy inference methods. View full abstract»

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  • Adaptive Fuzzy Interpolation

    Publication Year: 2011 , Page(s): 1107 - 1126
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1544 KB) |  | HTML iconHTML  

    Fuzzy interpolative reasoning strengthens the power of fuzzy inference by the enhancement of the robustness of fuzzy systems and the reduction of the systems' complexity. However, after a series of interpolations, it is possible that multiple object values for a common variable are inferred, leading to inconsistency in interpolated results. Such inconsistencies may result from defective interpolated rules or incorrect interpolative transformations. This paper presents a novel approach for identification and correction of defective rules in interpolative transformations, thereby removing the inconsistencies. In particular, an assumption-based truth-maintenance system (ATMS) is used to record dependences between interpolations, and the underlying technique that the classical general diagnostic engine (GDE) employs for fault localization is adapted to isolate possible faulty interpolated rules and their associated interpolative transformations. From this, an algorithm is introduced to allow for the modification of the original linear interpolation to become first-order piecewise linear. The approach is applied to a realistic problem, which predicates the diarrheal disease rates in remote villages, to demonstrate the potential of this study. View full abstract»

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  • An Output-Constrained Clustering Approach for the Identification of Fuzzy Systems and Fuzzy Granular Systems

    Publication Year: 2011 , Page(s): 1127 - 1140
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (722 KB) |  | HTML iconHTML  

    This paper presents an output-constrained clustering approach for fuzzy system identification and fuzzy granular system identification. The approach is unlike most existing clustering algorithms for structure identification of fuzzy systems, where the focus is on input or combined input-output clustering. The output-constrained clustering algorithm divides the output space into several partitions and each output partition is considered to be a constraint; then, input data are projected into clusters that are based on the input distribution constrained by the output partitions. By introducing the key concept of separability of a set of clusters within each output constraint, the proposed approach automatically finds an appropriate small and efficient number of clusters for each output constraint. To have an appropriate small and efficient number of clusters in each output constraint results in a more compact final system structure and better accuracy. This better performance is illustrated by experiments using benchmark function approximation and dynamic system identification. View full abstract»

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  • Granular Box Regression

    Publication Year: 2011 , Page(s): 1141 - 1152
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1207 KB) |  | HTML iconHTML  

    Granular computing (GrC) has gained increasing attention in the past decade. Although not uniquely defined, its basic idea is to approximate detailed machine-like information by a coarser presentation on a human-like level. Within granular computing, the mapping of continuous variables into intervals plays an important role. These intervals are often prerequisites for the formulation of linguistic variables. In this paper, we suggest a piecewise interval approximation and propose granular box regression. Its objective is to establish relationships between independent and dependent variables by multidimensional boxes. We interpret granular box regression as interval regression and show its potential for the extraction of fuzzy rules from data. In two experiments, we apply granular box regression to an artificial as well as to a real dataset in the field of finance and evaluate its properties. View full abstract»

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  • Quantized Control Design for Impulsive Fuzzy Networked Systems

    Publication Year: 2011 , Page(s): 1153 - 1162
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (405 KB) |  | HTML iconHTML  

    In this paper, a continuous-time Takagi-Sugeno (T-S) fuzzy system with impulsive effects that are controlled through network is investigated. Network signal-transmission delays and signal-quantization effects are simultaneously considered. The network is with two time-varying additive delays and limited capacity. First, a quantized output-feedback networked control system (NCS) model is established to describe the impulsive NCSs through a channel with limited capacity. Then, based on the Lyapunov-Krasovskii functional approach and a parallel-distributed compensation scheme, a delay-dependent stabilization approach is developed for the impulsive NCSs, which guarantees that the closed-loop system is asymptotically stable. Finally, a simulation example is given to illustrate the effectiveness of the proposed method. View full abstract»

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  • Supervised Hierarchical Clustering in Fuzzy Model Identification

    Publication Year: 2011 , Page(s): 1163 - 1176
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1244 KB) |  | HTML iconHTML  

    This paper presents a new, supervised, hierarchical clustering algorithm (SUHICLUST) for fuzzy model identification. The presented algorithm solves the problem of global model accuracy, together with the interpretability of local models as valid linearizations of the modeled nonlinear system. The algorithm combines the advantages of supervised, hierarchical algorithms, which are based on heuristic tree-construction algorithms, together with the advantages of fuzzy product space clustering. The high flexibility of the validity functions that is obtained by fuzzy clustering combined with supervised learning results in an efficient partitioning algorithm, which is independent of initialization and results in a parsimonious fuzzy model. Furthermore, the usability of SUHICLUST is very undemanding, because it delivers, in contrast with many other methods, reproducible results. In order to get reasonable results, the user only has to set either a threshold for the maximum number of local models or a value for the maximum allowed global model error as a termination criterion. For fine-tuning, the interpolation smoothness controls the degree of regularization. The performance is illustrated on both analytical examples and benchmark problems from the literature. View full abstract»

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  • 2011 Index IEEE Transactions on Fuzzy Systems Vol. 19

    Publication Year: 2011 , Page(s): 1177 - 1190
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2011 , Page(s): C3
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  • IEEE Transactions on Fuzzy Systems information for authors

    Publication Year: 2011 , Page(s): C4
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Aims & Scope

The IEEE Transactions on Fuzzy Systems (TFS) is published quarterly. TFS will consider papers that deal with the theory, design or an application of fuzzy systems ranging from hardware to software.

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Meet Our Editors

Editor-in-Chief
Chin-Teng Lin
National Chiao-Tung University