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Fuzzy Systems, IEEE Transactions on

Issue 6 • Date Dec. 2005

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Displaying Results 1 - 19 of 19
  • Table of contents

    Page(s): c1
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  • IEEE Transactions on Fuzzy Systems publication information

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  • Output feedback tracking control of MIMO systems using a fuzzy disturbance observer and its application to the speed control of a PM synchronous motor

    Page(s): 725 - 741
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (832 KB) |  | HTML iconHTML  

    One of the most important objectives in the design of control systems is to achieve the good tracking performance in the presence of the internal parameter uncertainty and external disturbance. In this paper, a new multiple-input-multiple-output (MIMO) fuzzy disturbance observer (FDO) based on output measurement is developed to achieve the goal. A filtered signal is introduced to resolve the algebraic loop encountered in the conventional FDO. The contribution of the disturbance observation error ζ to updating the parameters of the fuzzy system is analyzed in the sense of L2 and L. Then, the MIMO FDO is modified and the high gain observer (HGO) is employed to implement the output tracking control system. It is shown in a rigorous manner that the disturbance observation error, the tracking error and the state observation error converge to a compact set of which size can be kept arbitrarily small. Finally, the suggested method is applied to the speed control of a permanent magnet synchronous motor (PMSM) in the presence of the internal parameter uncertainty and external disturbance. The effectiveness and the feasibility of the suggested method are demonstrated by computer simulation. View full abstract»

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  • Fuzzy nonlinear regression with fuzzified radial basis function network

    Page(s): 742 - 760
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (848 KB) |  | HTML iconHTML  

    A fuzzified radial basis function network (FRBFN) is a kind of fuzzy neural network that is obtained by direct fuzzification of the well known neural model RBFN. A FRBFN contains fuzzy weights and can handle fuzzy-in fuzzy-out data. This paper shows that a FRBFN can also be interpreted as a kind of fuzzy expert system. Hence it owns the advantages of simple structure and clear physical meaning. Some metrics for fuzzy numbers have been extended to the metrics for n-dimensional fuzzy vectors, which are applicable to computations in FRBFNs. The corresponding metric spaces for n-dimensional fuzzy vectors are proved to be complete. Further, FRBFNs are proved to be able to act as universal function approximators for any continuous fuzzy function defined on a compact set. This paper applies the proposed FRBFN to nonparametric fuzzy nonlinear regression problems for multidimensional LR-type fuzzy data. Fuzzy nonlinear regression with FRBFNs can be formulated as a nonlinear mathematical programming problem. Two training algorithms are proposed to quickly solve the two types of problems under different criteria and constraint conditions, namely, the two-stage and BP (Back-Propagation) training algorithms. Simulation studies are carried out to verify the feasibility and demonstrate the advantages of the proposed approaches. View full abstract»

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  • Design of a multilevel fuzzy controller for nonlinear systems and stability analysis

    Page(s): 761 - 778
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (808 KB) |  | HTML iconHTML  

    In this paper, a multilevel fuzzy control (MLFC) system is developed and implemented to deal with the real-world nonlinear plants with intrinsic uncertainties and time-varying parameters. The proposed fuzzy control strategy has a hierarchical structure with an adaptation mechanism embedded in the lower level to tune the output membership functions (MFs) of the first layer fuzzy controller and can be used to control a system with an input-output monotonic relationship or a piecewise monotonic relationship. The stability of the closed-loop system under the proposed MLFC is theoretically proven. Simulations are carried out by applying the proposed multilevel fuzzy control (MLFC) to a uncertain nonlinear plants, and it is shown that much better system performances are achieved compared with conventional fuzzy logic controllers (FLC), even in presence of disturbance and noise. View full abstract»

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  • Decentralized PDC for large-scale T-S fuzzy systems

    Page(s): 779 - 786
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB) |  | HTML iconHTML  

    This paper studies the decentralized stabilization problem for a large-scale system. The considered large-scale system comprises of a number of subsystems and each subsystem is represented by a Takagi-Sugeno (T-S) fuzzy model. The interconnection between any two subsystems may be nonlinear and satisfies some matching condition. By the concept of parallel distributed compensation (PDC), the decentralized fuzzy control for each subsystem is synthesized, in which the control gain depends on the strength of interconnections, maximal number of the fired rules in each subsystem, and the common positive matrix Pi. Based on Lyapunov criterion and Riccati-inequality, some sufficient conditions are derived and the common Pi is solved by linear matrix inequalities (LMI) so that the whole closed loop large-scale fuzzy system with the synthesized fuzzy control is asymptotically stable. Finally, a numerical example is given to illustrate the control synthesis and its effectiveness. View full abstract»

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  • Stabilization of uncertain fuzzy time-delay systems via variable structure control approach

    Page(s): 787 - 798
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (472 KB) |  | HTML iconHTML  

    In view of a recent new application of variable structure control (VSC) to the stabilization problem for Takagi-Sugeno (T-S) fuzzy models, this paper aims to study the stabilization of uncertain fuzzy time-delay systems in T-S fuzzy model via VSC approach. There are mainly two features in this paper: one lies in the incorporation of time-delays (both smooth and nonsmooth delays) in which case Lyapunov functionals and Razumikhin Theorem are required to solve the stabilization problem; the other feature is that not only matched uncertainties but also mismatched uncertainties in the state variables are considered. As a sequence, the contribution of this paper consists of various control schemes proposed for the VSC design and the present results are in terms of linear matrix inequalities (LMIs). An illustrative example is given to show the effectiveness of our various results. View full abstract»

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  • NFI: a neuro-fuzzy inference method for transductive reasoning

    Page(s): 799 - 808
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    This paper introduces a novel neural fuzzy inference method-NFI for transductive reasoning systems. NFI develops further some ideas from DENFIS-dynamic neuro-fuzzy inference systems for both online and offline time series prediction tasks. While inductive reasoning is concerned with the development of a model (a function) to approximate data in the whole problem space (induction), and consecutively-using this model to predict output values for a new input vector (deduction), in transductive reasoning systems a local model is developed for every new input vector, based on some closest to this vector data from an existing database (also generated from an existing model). NFI is compared with both inductive connectionist systems (e.g., MLP, DENFIS) and transductive reasoning systems (e.g., K-NN) on three case study prediction/identification problems. The first one is a prediction task on Mackey Glass time series; the second one is a classification on Iris data; and the last one is a real medical decision support problem of estimating the level of renal function of a patient, based on measured clinical parameters for the purpose of their personalised treatment. The case studies have demonstrated better accuracy obtained with the use of the NFI transductive reasoning in comparison with the inductive reasoning systems. View full abstract»

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  • Fuzzy rule interpolation for multidimensional input spaces with applications: a case study

    Page(s): 809 - 819
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications. View full abstract»

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  • A new fuzzy support vector machine to evaluate credit risk

    Page(s): 820 - 831
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and the business community. In this paper, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, our new fuzzy support vector machine treats every sample as both positive and negative classes, but with different memberships. By this way we expect the new fuzzy support vector machine to have more generalization ability, while preserving the merit of insensitive to outliers, as the fuzzy support vector machine (SVM) proposed in previous papers. We reformulate this kind of two-group classification problem into a quadratic programming problem. Empirical tests on three public datasets show that it can have better discriminatory power than the standard support vector machine and the fuzzy support vector machine if appropriate kernel and membership generation method are chosen. View full abstract»

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  • Fuzzy approximate disturbance decoupling of MIMO nonlinear systems by backstepping and application to chemical processes

    Page(s): 832 - 847
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (584 KB) |  | HTML iconHTML  

    Fuzzy approximate disturbance decoupling concept is introduced for a class of multiple-input-multiple-output (MIMO) nonlinear systems with completely unknown nonlinearities. Based on backstepping technique, a fuzzy almost disturbance decoupling control scheme is proposed. The fuzzy controllers guarantee internal uniform ultimate boundedness of the closed-loop adaptive systems and render a bounded approximate L2 gain from the disturbance input to the output. The developed design scheme is applied to control a two continuous stirred tank reactor process. The simulation results illustrate the effectiveness of the method proposed in this paper. View full abstract»

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  • A probabilistic fuzzy logic system for modeling and control

    Page(s): 848 - 859
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (608 KB) |  | HTML iconHTML  

    In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance. View full abstract»

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  • Quality without compromise [advertisement]

    Page(s): 861
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  • Put your technology leadership in writing

    Page(s): 862
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  • IEEE Member Digital Library

    Page(s): 863
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  • 2005 Index

    Page(s): 864 - 872
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  • IEEE Computational Intelligence Society Information

    Page(s): c3
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  • IEEE Transactions on Fuzzy Systems Information for authors

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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