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Fuzzy Systems, IEEE Transactions on

Issue 1 • Date Feb. 2005

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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

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  • Adaptive-tree-structure-based fuzzy inference system

    Page(s): 1 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (592 KB) |  | HTML iconHTML  

    A new fuzzy inference system named adaptive-tree-structure-based fuzzy inference system (ATSFIS) is proposed, which is abbreviated as fuzzy tree (FT). The fuzzy partition of input data set and the membership function of every subset are obtained by means of the fuzzy binary tree structure based algorithm. Two structures of FT, FT-I, and FT-II, are presented. The characteristics of FT are: 1) The parameters of antecedent and consequent for a Takagi-Sugeno fuzzy model are learned simultaneously; and 2) The fuzzy partition of input data set is adaptive to the pattern of data distribution to optimize the number of the subsets automatically. The main advantage of FT is more suitable to solve the problems, for which the number of input dimension is large, since by using the fuzzy binary tree, every farther set will be partitioned into only two subsets no matter how large the input dimension is. Therefore, in some sense the "rule explosion" will be avoided possibly. In comparison with some existing fuzzy inference systems, it is shown that the FT is also of less computation and high accuracy. The advantages of FT are illustrated by simulation results. View full abstract»

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  • Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction

    Page(s): 13 - 29
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1072 KB) |  | HTML iconHTML  

    Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely developed task. It involves adjusting some of the components of the knowledge base without completely redefining it. This contribution introduces a genetic tuning process for jointly fitting the fuzzy rule symbolic representations and the meaning of the involved membership functions. To adjust the former component, we propose the use of linguistic hedges to perform slight modifications keeping a good interpretability. To alter the latter component, two different approaches changing their basic parameters and using nonlinear scaling factors are proposed. As the accomplished experimental study shows, the good performance of our proposal mainly lies in the consideration of this tuning approach performed at two different levels of significance. The paper also analyzes the interaction of the proposed tuning method with a fuzzy rule set reduction process. A good interpretability-accuracy tradeoff is obtained combining both processes with a sequential scheme: first reducing the rule set and subsequently tuning the model. View full abstract»

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  • Robust fuzzy control for a plant with fuzzy linear model

    Page(s): 30 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (408 KB) |  | HTML iconHTML  

    A robust complexity reduced proportional-integral-derivative (PID)-like fuzzy controllers is designed for a plant with fuzzy linear model. The plant model is described with the expert's linguistic information involved. The linguistic information for the plant model is represented as fuzzy sets. In order to design a robust fuzzy controller for a plant model with fuzzy sets, an approach is developed to implement the best crisp approximation of fuzzy sets into intervals. Then, Kharitonov's Theorem is applied to construct a robust fuzzy controller for the fuzzy uncertain plant with interval model. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controller is significantly reduced. The parameters in the robust fuzzy controller are determined to satisfy the stability conditions. The robustness of the designed fuzzy controller is discussed. Also, with the provided definition of relative robustness, the robustness of the complexity reduced fuzzy controller is compared to the classical PID controller for a second-order plant with fuzzy linear model. The simulation results are included to show the effectiveness of the designed PID-like robust fuzzy controller with the complexity reduced fuzzy mechanism. View full abstract»

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  • Control of convergence in a computational fluid dynamics simulation using ANFIS

    Page(s): 42 - 47
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    Adaptive-network-based fuzzy inference system (ANFIS) was used to control convergence of a computational fluid dynamics (CFD) algorithm. Normalized residuals were used to select the relaxation factors on each iteration of the computation. The ratio between the residual of the current iteration and of the previous iteration was used as a control input. ANFIS was designed to keep the tuning index at or near one throughout the fluid dynamics simulation. The finite volume CFD algorithm SIMPLER was used as the platform for this study. Four different benchmark cases were examined. View full abstract»

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  • Constructive granular systems with universal approximation and fast knowledge discovery

    Page(s): 48 - 57
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (376 KB) |  | HTML iconHTML  

    Conventional gradient descent learning algorithms for soft computing systems have the learning speed bottleneck problem and the local minima problem. To effectively solve the two problems, the n-variable constructive granular system with high-speed granular constructive learning is proposed based on granular computing and soft computing, and proved to be a universal approximator. The fast granular constructive learning algorithm can highly speed up granular knowledge discovery by directly calculating all parameters of the n-variable constructive granular system using training data, and then construct the n-variable constructive granular system with any required accuracy using a small number of granular rules. Predictive granular knowledge discovery simulation results indicate that the direct-calculation-based granular constructive algorithm is better than the conventional gradient descent learning algorithm in terms of learning speed, learning error, and prediction error. View full abstract»

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  • Fuzzy predictive control of a solar power plant

    Page(s): 58 - 68
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1352 KB) |  | HTML iconHTML  

    This work presents the application of fuzzy predictive control to a solar power plant. The proposed predictive controller uses fuzzy characterization of goals and constraints, based on the fuzzy optimization framework for multi-objective satisfaction problems. This approach enhances model based predictive control (MBPC) allowing the specification of more complex requirements. A brief description of the solar power plant and its simulator is given. Basic concepts of predictive control and fuzzy predictive control are introduced. Two fuzzy predictive controllers using different membership functions are designed for a solar power plant, and they are compared with a classical predictive controller. The simulation results show that the fuzzy MBPC formulation, based on a well proven successful algorithm, gives a greater flexibility to characterize the goals and constraints than classical control. View full abstract»

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  • An input-output clustering approach to the synthesis of ANFIS networks

    Page(s): 69 - 81
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (680 KB) |  | HTML iconHTML  

    A useful neural network paradigm for the solution of function approximation problems is represented by adaptive neuro-fuzzy inference systems (ANFIS). Data driven procedures for the synthesis of ANFIS networks are typically based on clustering a training set of numerical samples of the unknown function to be approximated. Some serious drawbacks often affect the clustering algorithms adopted in this context, according to the particular data space where they are applied. To overcome such problems, we propose a new ANFIS synthesis procedure where clustering is applied in the joint input-output data space. Using this approach, it is possible to determine the consequent part of Sugeno first-order rules and therefore the hyperplanes characterizing the local structure of the function to be approximated. Successively, the fuzzy antecedent part of each rule is determined using a particular fuzzy min-max classifier, which is based on the adaptive resolution mechanism. The generalization capability of the resulting ANFIS architecture is optimized using a constructive procedure for the automatic determination of the optimal number of rules. Simulation tests and comparisons with respect to other neuro-fuzzy techniques are discussed in the paper, in order to assess the efficiency of the proposed approach. View full abstract»

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  • Robust H control for uncertain discrete-time-delay fuzzy systems via output feedback controllers

    Page(s): 82 - 93
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB) |  | HTML iconHTML  

    This work investigates the problem of robust output feedback H control for a class of uncertain discrete-time fuzzy systems with time delays. The state-space Takagi-Sugeno fuzzy model with time delays and norm-bounded parameter uncertainties is adopted. The purpose is the design of a full-order fuzzy dynamic output feedback controller which ensures the robust asymptotic stability of the closed-loop system and guarantees an H norm bound constraint on disturbance attenuation for all admissible uncertainties. In terms of linear matrix inequalities (LMIs), a sufficient condition for the solvability of this problem is presented. Explicit expressions of a desired output feedback controller are proposed when the given LMIs are feasible. The effectiveness and the applicability of the proposed design approach are demonstrated by applying this to the problem of robust H control for a class of uncertain nonlinear discrete delay systems. View full abstract»

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  • H controller synthesis of fuzzy dynamic systems based on piecewise Lyapunov functions and bilinear matrix inequalities

    Page(s): 94 - 103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (440 KB) |  | HTML iconHTML  

    This work presents an H controller design method for fuzzy dynamic systems based on techniques of piecewise smooth Lyapunov functions and bilinear matrix inequalities. It is shown that a piecewise continuous Lyapunov function can be used to establish the global stability with H performance of the resulting closed-loop fuzzy control systems and the control laws can be obtained by solving a set of bilinear matrix inequalities (BMIs). Two examples are given to illustrate the application of the proposed methods. View full abstract»

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  • Adaptive fuzzy H stabilization for strict-feedback canonical nonlinear systems via backstepping and small-gain approach

    Page(s): 104 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (448 KB) |  | HTML iconHTML  

    A novel adaptive fuzzy controller with H performance is proposed for a wide class of strict-feedback canonical nonlinear systems. The systems may possess a class of uncertainties referred to as unstructured uncertain functions, which are not linearly parameterized and have no prior knowledge of the bound. The Takagi-Sugeno-type fuzzy logic systems are used to approximate the uncertainties and a systematic design procedure is developed for synthesis of adaptive fuzzy control with H performance, which combines the backstepping technique and generalized small gain approach. The method preserves the three advantages, those are, the semiglobal uniform ultimate bound of adaptive control in the presence of unstructured uncertainties can be guaranteed, the adaptive mechanism with only one learning parameter is obtained and the possible controller singularity problem in some of the existing adaptive control schemes with feedback linearization techniques can be removed. Performance and limitations of proposed method are discussed and illustrated with simulation results. View full abstract»

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  • Analysis and design of an affine fuzzy system via bilinear matrix inequality

    Page(s): 115 - 123
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB) |  | HTML iconHTML  

    A novel analysis and design method for affine fuzzy systems is proposed. Both continuous-time and discrete-time cases are considered. The quadratic stability and stabilizability conditions of the affine fuzzy systems are derived and they are represented in the formulation of bilinear matrix inequalities (BMIs). Two diffeomorphic state transformations (one is linear and the other is nonlinear) are introduced to convert the plant into more tractable affine form. The conversion makes the stability and stabilizability problems of the affine fuzzy systems convex and makes the problems solvable directly by the convex linear matrix inequality (LMI) technique. The bias terms of the fuzzy controller are solved simultaneously together with the gains. Finally, the applicability of the suggested method is demonstrated via an example and computer simulation. View full abstract»

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  • About projection-selection-join queries addressed to possibilistic relational databases

    Page(s): 124 - 139
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB) |  | HTML iconHTML  

    This work deals with databases containing ill-known attribute values represented by possibility distributions. Any such database has a canonical interpretation as a set of regular databases, but it is well known that their manipulation raises a number of problems, in particular with respect to the soundness of querying operations and the tractability of the evaluation process. In This work, we propose a query language including three operators which are soundly defined on extended possibilistic (compact) relations, which is the key for tractability. The originality of the approach is twofold: 1) a nesting mechanism is introduced in the data model in order to support the expression of the result of some of the operations allowed, and 2) a joint operation enables to compose possibilistic relations under some reasonable hypotheses. View full abstract»

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  • Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems

    Page(s): 140 - 151
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1336 KB) |  | HTML iconHTML  

    We introduce a new class of operators called quasi-triangular norms. They are denoted by H and parameterized by a parameter ν:H(a1,a2,...,an;ν). From the construction of function H, it follows that it becomes a t-norm for ν=0 and a dual t-conorm for ν=1. For ν close to 0, function H resembles a t-norm and for ν close to 1, it resembles a t-conorm. In the paper, we also propose adjustable quasi-implications and a new class of neuro-fuzzy systems. Most neuro-fuzzy systems proposed in the past decade employ "engineering implications" defined by a t-norm as the minimum or product. In our proposition, a quasi-implication I(a,b;ν) varies from an "engineering implication" T{a,b} to corresponding S-implication as ν goes from 0 to 1. Consequently, the structure of neuro-fuzzy systems presented in This work is determined in the process of learning. Learning procedures are derived and simulation examples are presented. View full abstract»

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  • Robust stabilization of nonlinear multiple time-delay large-scale systems via decentralized fuzzy control

    Page(s): 152 - 163
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    To overcome the effect of modeling errors between nonlinear multiple time-delay subsystems and Takagi-Sugeno (T-S) fuzzy models with multiple time delays, a robustness design of fuzzy control is proposed in This work. In terms of Lyapunov's direct method, a delay-dependent stability criterion is hence derived to guarantee the asymptotic stability of nonlinear multiple time-delay large-scale systems. Based on this criterion and the decentralized control scheme, a set of model-based fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time-delay large-scale system. Finally, a numerical example with simulations is given to demonstrate the concepts discussed throughout This work. View full abstract»

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  • Optimality test for generalized FCM and its application to parameter selection

    Page(s): 164 - 176
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    In cluster analysis, the fuzzy c-means (FCM) clustering algorithm is the best known and most widely used method. It was proven to converge to either a local minimum or saddle points by Bezdek et al. Wei and Mendel produced efficient optimality tests for FCM fixed points. Recently, a weighting exponent selection for FCM was proposed by Yu et al. Inspired by these results, we unify several alternative FCM algorithms into one model, called the generalized fuzzy c-means (GFCM). This GFCM model presents a wide variation of FCM algorithms and can easily lead to new and interesting clustering algorithms. Moreover, we construct a general optimality test for GFCM fixed points. This is applied to theoretically choose the parameters in the GFCM model. The experimental results demonstrate the precision of the theoretical analysis. View full abstract»

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  • Quality without compromise [advertisement]

    Page(s): 177
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  • Leading the field since 1884 [advertisement]

    Page(s): 178
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  • IEEE copyright form

    Page(s): 179 - 180
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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