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Fuzzy Systems, IEEE Transactions on

Issue 1 • Date Feb. 2013

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Displaying Results 1 - 22 of 22
  • Table of Contents

    Page(s): C1
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  • IEEE Transactions on Fuzzy Systems publication information

    Page(s): C2
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  • A Vectorization-Optimization-Method-Based Type-2 Fuzzy Neural Network for Noisy Data Classification

    Page(s): 1 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1393 KB) |  | HTML iconHTML  

    This paper proposes a vectorization-optimization-method (VOM)-based type-2 fuzzy neural network (VOM2FNN) for noisy data classification. In handling problems with uncertainties, such as noisy data, type-2 fuzzy systems usually outperform their type-1 counterparts. Hence, type-2 fuzzy sets are adopted in the antecedent parts to model the uncertainty. To consider the classification problems, the discriminative capability is crucial to determine the performance. Therefore, a VOM is proposed in the consequent parts to increase the discriminability and reduce the parameters. Compared with other existing fuzzy neural networks, the novelty of the proposed VOM2FNN is its consideration of both uncertainty and discriminability. The effectiveness of the proposed VOM2FNN is demonstrated by three classification problems. Experimental results and theoretical analysis indicate that the proposed VOM2FNN performs better than the other fuzzy neural networks. View full abstract»

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  • Petri Net Representation of Switched Fuzzy Systems

    Page(s): 16 - 29
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1999 KB) |  | HTML iconHTML  

    Switched fuzzy systems can be used to describe the hybrid systems with fuzziness. However, the languages to describe the switching logic and the fuzzy subsystems are, in general, different, and this difference makes the system analysis and implementation hard. In this paper, we use differential Petri net (DPN) as a unified model to represent both the discrete logic and fuzzy dynamic processes. To exam the rationality of the representation, we prove the correctness of the representation for the discrete part and estimate the approximation accuracy of the representation for the dynamic part. Our work provides a way to analyze the switched fuzzy systems by checking the PN and to use the PN model for further system implementation. We demonstrate the benefits of our work via a case study. View full abstract»

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  • A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

    Page(s): 30 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1941 KB) |  | HTML iconHTML  

    The majority of reported learning methods for Takagi-Sugeno-Kang (TSK) fuzzy neural models to date mainly focus on improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behavior when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, identify the corresponding consequent models which can be directly explained in terms of system behavior, presents a critical step in fuzzy neural modeling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practiced in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. Thus, a new Jacobian matrix is proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues regarding the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods. View full abstract»

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  • A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions

    Page(s): 45 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1436 KB) |  | HTML iconHTML  

    Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented. View full abstract»

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  • A Review of Fuzzy Cognitive Maps Research During the Last Decade

    Page(s): 66 - 79
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (427 KB) |  | HTML iconHTML  

    This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years. View full abstract»

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  • Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Systems: Overview and Comparisons

    Page(s): 80 - 99
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2136 KB) |  | HTML iconHTML  

    Interval type-2 fuzzy logic systems (IT2 FLSs) have demonstrated better abilities to handle uncertainties than their type-1 (T1) counterparts in many applications; however, the high computational cost of the iterative Karnik-Mendel (KM) algorithms in type-reduction means that it is more expensive to deploy IT2 FLSs, which may hinder them from certain cost-sensitive real-world applications. This paper provides a comprehensive overview and comparison of three categories of methods to reduce their computational cost. The first category consists of five enhancements to the KM algorithms, which are the most popular type-reduction algorithms to date. The second category consists of 11 alternative type-reducers, which have closed-form representations and, hence, are more convenient for analysis. The third category consists of a simplified structure for IT2 FLSs, which can be combined with any algorithms in the first or second category for further computational cost reduction. Experiments demonstrate that almost all methods in these three categories are faster than the KM algorithms. This overview and comparison will help researchers and practitioners on IT2 FLSs choose the most suitable structure and type-reduction algorithms, from a computational cost perspective. A recommendation is given in the conclusion. View full abstract»

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  • Evolutionary Robot Wall-Following Control Using Type-2 Fuzzy Controller With Species-DE-Activated Continuous ACO

    Page(s): 100 - 112
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1030 KB) |  | HTML iconHTML  

    This paper proposes evolutionary wall-following control of a mobile robot using an interval type-2 fuzzy controller (IT2FC) with species-differential-evolution-activated continuous ant colony optimization (SDE-CACO). Both the position and speed of a mobile robot are controlled by using two IT2FCs to improve noise resistance ability. A new cost function is defined to accurately evaluate the wall-following performance of an evolutionary IT2FC. A two-stage training approach is proposed that learns a position IT2FC followed by a speed IT2FC to optimize both the wall-following accuracy and the moving speed. The proposed learning approach avoids the time consuming task of the exhaustive collection of supervised input-output training pairs. All fuzzy rules are generated online using a clustering-based approach during the evolutionary learning process. All of the free parameters in an online-generated IT2FC are optimized using SDE-CACO, in which an SDE mutation operation is incorporated within a continuous ACO to improve its explorative ability. The proposed SDE-CACO is compared with various population-based optimization algorithms to demonstrate its efficiency and effectiveness in the wall-following control problem. This study also includes experiments that demonstrate wall-following control utilizing a real mobile robot. View full abstract»

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  • Preference Relations Based on Intuitionistic Multiplicative Information

    Page(s): 113 - 133
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (725 KB) |  | HTML iconHTML  

    Preference relations are powerful techniques to express the preferences over alternatives (or criteria) and mainly fall into two categories: fuzzy preference relations (also called reciprocal preference relations) and multiplicative preference relations. For a pair of alternatives, a fuzzy preference relation only gives the degree that an alternative is prior to another; thus, the intuitionistic fuzzy preference relation is introduced by adding the degree that an alternative is not prior to another, which can describe the preferences over two alternatives more comprehensively. However, the intuitionistic fuzzy preference uses the symmetrical scale to express the decision makers' preference relations, which are inconsistent with our intuition in some situations. If we use the unsymmetrical scale to express the preferences about two alternatives instead of the symmetrical scale in intuitionistic fuzzy preference relation, then a new concept is introduced, which we call the intuitionistic multiplicative preference relation reflecting our intuition more objectively. In this paper, we study the aggregation of intuitionistic multiplicative preference information, propose some aggregation techniques, investigate their properties, and apply them to decision making based on intuitionistic multiplicative preference relations. View full abstract»

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  • Adaptive Fuzzy Output Feedback Control of MIMO Nonlinear Systems With Unknown Dead-Zone Inputs

    Page(s): 134 - 146
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (997 KB) |  | HTML iconHTML  

    This paper is concerned with the problem of adaptive fuzzy tracking control for a class of multi-input and multi-output (MIMO) strict-feedback nonlinear systems with both unknown nonsymmetric dead-zone inputs and immeasurable states. In this research, fuzzy logic systems are utilized to evaluate the unknown nonlinear functions, and a fuzzy adaptive state observer is established to estimate the unmeasured states. Based on the information of the bounds of the dead-zone slopes as well as treating the time-varying inputs coefficients as a system uncertainty, a new adaptive fuzzy output feedback control approach is developed via the backstepping recursive design technique. It is shown that the proposed control approach can assure that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded. It is also shown that the observer and tracking errors converge to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach. View full abstract»

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  • Control Synthesis of Discrete-Time T–S Fuzzy Systems Based on a Novel Non-PDC Control Scheme

    Page(s): 147 - 157
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (887 KB) |  | HTML iconHTML  

    This paper proposes relaxed stabilization conditions of discrete-time nonlinear systems in the Takagi-Sugeno (T-S) fuzzy form. By using the algebraic property of fuzzy membership functions, a novel nonparallel distributed compensation (non-PDC) control scheme is proposed based on a new class of fuzzy Lyapunov functions. Thus, relaxed stabilization conditions for the underlying closed-loop fuzzy system are developed by applying a new slack variable technique. In particular, some existing fuzzy Lyapunov functions and non-PDC control schemes are special cases of the new Lyapunov function and fuzzy control scheme, respectively. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method. View full abstract»

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  • Robust Observer Design for Unknown Inputs Takagi–Sugeno Models

    Page(s): 158 - 164
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (297 KB) |  | HTML iconHTML  

    This paper deals with the observer design for Takagi-Sugeno (T-S) fuzzy models subject to unknown inputs and disturbance affecting both states and outputs of the system. Sufficient conditions to design an unknown input T-S observer are given in linear matrix inequality (LMI) terms. Both continuous-time and discrete-time cases are studied. Relaxations are introduced by using intermediate variables. Extension to the case of unmeasured decision variables is also given. A numerical example is given to illustrate the effectiveness of the given results. View full abstract»

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  • To Transmit or Not to Transmit: A Discrete Event-Triggered Communication Scheme for Networked Takagi–Sugeno Fuzzy Systems

    Page(s): 164 - 170
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (565 KB) |  | HTML iconHTML  

    This paper first proposes a discrete event-triggered communication scheme for a class of networked Takagi-Sugeno (T-S) fuzzy systems. This scheme has two main features: 1) Whether or not the sampled state should be transmitted is determined by the current-sampled state and the error between the current-sampled state and the latest transmitted state. Compared with those in a periodic time-triggered communication scheme, the communication bandwidth utilization is considerably reduced while preserving the desired control performance; and 2) it is a discrete event-triggered communication scheme due to the fact that the triggered conditions are only measured and checked at a constant sampling period. Compared with a continuous event-triggered communication scheme, the special hardware for continuous measurement and computation is no longer needed. Second, a networked T-S fuzzy model is delicately constructed, which not only considers nonuniform time scales in the networked T-S fuzzy model and the parallel distributed compensation fuzzy control rules but includes the aforementioned state error as well. Third, a stability criterion and a stabilization criterion about the networked T-S fuzzy system are derived, respectively. The stability criterion and stabilization criterion can provide a tradeoff to balance the required communication resource and the desired performance: Lowering the desired performance allows the network to allocate more limited bandwidth to other nodes in need. Finally, a numerical example is given to show the effectiveness of the proposed method. View full abstract»

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  • Fuzzy Nash Equilibriums in Crisp and Fuzzy Games

    Page(s): 171 - 176
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (508 KB) |  | HTML iconHTML  

    In this paper, we introduce fuzzy Nash equilibrium to determine a graded representation of Nash equilibriums in crisp and fuzzy games. This interpretation shows the distribution of equilibriums in the matrix form of a game and handles uncertainties in payoffs. In addition, a new method to rank fuzzy values with the user's viewpoint is investigated. By this means, the definition of satisfaction function, which provides the result of comparison in the form of real value, is developed when users have preferences regarding the payoffs. View full abstract»

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  • A Three-Domain Fuzzy Wavelet System for Simultaneous Processing of Time-Frequency Information and Fuzziness

    Page(s): 176 - 183
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (411 KB) |  | HTML iconHTML  

    Traditional wavelet system is a two-domain (time and frequency domains) wavelet system (2DWS), which works only in time and frequency domains. The 2DWS is not able to treat time-frequency information and fuzziness simultaneously. For this reason, a three-domain (fuzzy, time, and frequency domains) fuzzy wavelet system (3DFWS) is proposed, where the three-domain mechanism provides a solution to handle fuzzy uncertainties and time-frequency information together. The major advantage of 3DFWS is able to use the prior knowledge via the novel fuzzy domain to analyze uncertain data and signals, which will enhance the potentials of 2DWS. Experimental and simulation studies show that the performance of the proposed 3DFWS is superior to the traditional one for simultaneous processing of time-frequency and fuzziness. View full abstract»

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  • A Fuzzy Measure Similarity Between Sets of Linguistic Summaries

    Page(s): 183 - 189
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    In this paper, we consider the problem of evaluating the similarity of two sets of linguistic summaries of sensor data. Huge amounts of available data cause a dramatic need for summarization. In continuous monitoring, it is useful to compare one time interval of data with another, for example, to detect anomalies or to predict the onset of a change from a normal state. Assuming that summaries capture the essence of the data, it is sufficient to compare only those summaries, i.e., they are descriptive features for recognition. In previous work, we developed a similarity measure between two individual summaries and proved that the associated dissimilarity is a metric. Additionally, we proposed some basic methods to combine these similarities into an aggregate value. Here, we develop a novel parameter free method, which is based on fuzzy measures and integrals, to fuse individual similarities that will produce a closeness measurement between sets of summaries. We provide a case study from the eldercare domain where the goal is to compare different nighttime patterns for change detection. The reasons for studying linguistic summaries for eldercare are twofold: First, linguistic summaries are the natural communication tool for health care providers in a decision support system, and second, due to the extremely large volume of raw data, these summaries create compact features for an automated reasoning for detection and prediction of health changes as part of the decision support system. View full abstract»

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  • Open Access

    Page(s): 190
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  • IEEE Xplore Digital Library

    Page(s): 191
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  • IEEE Foundation

    Page(s): 192
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  • IEEE Computational Intelligence Society Information

    Page(s): C3
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  • IEEE Transactions on Fuzzy Systems information for authors

    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