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Fuzzy Systems, IEEE Transactions on

Issue 1 • Date Feb. 2003

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Displaying Results 1 - 14 of 14
  • Errata on "GA-fuzzy modeling and classification: complexity and performance" and "Compact and transparent fuzzy models and classifiers through iterative complexity reduction"

    Publication Year: 2003 , Page(s): 151
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (148 KB)  

    The authors would like to correct a typing error that repeatedly appeared in their work in [ibid., vol. 8, pp. 509??522, Oct. 2000] and [ibid., vol. 9, pp. 516??524, Aug. 2001], and, thereafter, also in J. Nunez-Garcia and O. Wolkenhauer's "Random set system identification," [IEEE Trans. Fuzzy Syst., vol. 10, pp. 287??296, June 2002] and the authors' contribution "The practical handbook of genetic algorithms: Applications" [in Compact Fuzzy Models and Classifiers Through Model Reduction and Evolutionary Optimization, 2nd ed, L. Chambers, Ed. Boca Raton, FL: CRC, 2000, ch. 1, pp. 31??59]. Namely, in several papers, they applied the data from L. Wang and J. Yen (1998 & 1999) as a benchmark problem. Unfortunately, the dynamic model equation (1), corrected here, was incorrectly repeated and duplicated due to a typing error that appeared in the first of the above-named articles, i.e., a '+' operator is missing in [eq. 11, p. 513]. Thereafter, it was wrongly duplicated in the second named article [eq. 13, p. 519], and in the other works as noted. Concluding, the correct equation (1) is given here. View full abstract»

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  • GA-based intelligent digital redesign of fuzzy-model-based controllers

    Publication Year: 2003 , Page(s): 35 - 44
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (592 KB) |  | HTML iconHTML  

    Intelligent digital redesign involves converting a continuous-time fuzzy-model-based controller into an equivalent discrete-time counterpart for the digital control of continuous-time nonlinear systems by using the Takagi-Sugeno (TS) fuzzy models. In this paper, the authors present a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs). More precisely, the intelligent digital redesign problem is converted to an equivalent optimization problem, and then GAs are adopted to find a solution. The search space, in which each problem variable is defined for GAs, are systematically obtained by the interval arithmetic operations. The proposed method results in global matching of the states of the analogously controlled system with those of the digitally controlled system while the conventional intelligent digital redesign method does not. The Chen's chaotic system is used as an illustrative example to show the effectiveness and the feasibility of the developed method. The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers. View full abstract»

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  • Matrix-pattern-based computer algorithm for solving fuzzy relation equations

    Publication Year: 2003 , Page(s): 100 - 108
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (600 KB) |  | HTML iconHTML  

    This paper proposes a new computer algorithm to solve the fuzzy relation equation P&ogr;Q=R, where &ogr; denotes max-min composition or max-product composition. This algorithm operates systematically and graphically on a matrix pattern to get all the solutions of P. Furthermore, by using MATLAB software 6.0, the algorithm is implemented in a computer program attached in the appendix of this paper. Two examples are given to illustrate the effectiveness of the proposed algorithm. View full abstract»

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  • A framework for fuzzy quantification models analysis

    Publication Year: 2003 , Page(s): 89 - 99
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (685 KB) |  | HTML iconHTML  

    A framework for description of fuzzy quantification models is presented. Within this framework, the fuzzy quantified statements evaluation problem is described as the compatibility between the fuzzy quantifier and a fuzzy cardinality or a fuzzy aggregation measure. A list of desirable properties for quantification models is presented and those models that fit the framework are confronted with it. View full abstract»

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  • Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation

    Publication Year: 2003 , Page(s): 57 - 67
    Cited by:  Papers (93)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (884 KB) |  | HTML iconHTML  

    Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear system subject to actuator saturation is presented. In our TS fuzzy representation, the modeling error is also captured by norm-bounded uncertainties. A set invariance condition for the system in the TS fuzzy representation is first established. Based on this set invariance condition, the problem of estimating the domain of attraction of a TS fuzzy system under a constant state feedback law is formulated and solved as a linear matrix inequality (LMI) optimization problem. By viewing the state feedback gain as an extra free parameter in the LMI optimization problem, we arrive at a method for designing state feedback gain that maximizes the domain of attraction. A fuzzy scheduling control design method is also introduced to further enlarge the domain of attraction. An inverted pendulum is used to show the effectiveness of the proposed fuzzy controller. View full abstract»

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  • Two-rule-based linguistic fuzzy controllers

    Publication Year: 2003 , Page(s): 79 - 88
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (795 KB)  

    This paper presents a new methodology to design two- or three-input-single-output fuzzy logic controllers (FLCs). The main features of the proposed methodology are: 1) the rule-bases are not complete and consist of only two rules relating fuzzy descriptions of not-self correcting errors to the sign of control actions, and 2) appropriate choices of input and output membership functions together with a fuzzy reasoning method lead to completeness of the controllers. The paper also focuses on the use of a quasilinear-mean (q-l-m) operator as soft implementation of the logical connective "and". FLCs derived in this way are characterized by input-output mappings which are smooth, easy-to-analyze, and easy-to-implement. This paper also includes a comparison with conventional three-term controllers and N×N-rule-based FLCs. Some simulation results are also proposed. View full abstract»

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  • Fuzzy redundancy resolution and motion coordination for underwater vehicle-manipulator systems

    Publication Year: 2003 , Page(s): 109 - 120
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (808 KB) |  | HTML iconHTML  

    The problem of redundancy resolution and motion coordination between the vehicle and the manipulator in underwater vehicle-manipulator systems (UVMSs) is addressed in this paper. UVMSs usually possess more degrees of freedom than those required to perform end-effector tasks; therefore, they are redundant systems and kinematic control techniques can be applied aimed at achieving additional control objectives besides tracking of the end-effector trajectory. In this paper, a task-priority inverse kinematics approach to redundancy resolution is merged with a fuzzy technique to manage the vehicle-arm coordination. The fuzzy technique is used both to distribute the motion between vehicle and manipulator and to handle multiple secondary tasks. Numerical case studies are developed to demonstrate effectiveness of the proposed technique. View full abstract»

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  • Balancing meals using fuzzy arithmetic and heuristic search algorithms

    Publication Year: 2003 , Page(s): 68 - 78
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (946 KB) |  | HTML iconHTML  

    This paper aims at showing how well-known ideas in the fields of fuzzy arithmetic and heuristic search have been combined in an educational software in nutrition in order to provide not only a better mathematical modeling, but also significant functional improvements for end-users, comparing to other nutrition programs. This software, called Nutri-Expert, helps patients to improve their nutritional habits, by analyzing in detail their food intakes, and by suggesting changes that result in well-balanced meals. Fuzzy arithmetic is used to model the input and database data, and for all computations. A fuzzy pattern matching is performed between total amounts of nutrients and different norm patterns, and the results are displayed using a galvanometer metaphor. A heuristic search algorithm is used to find out minimal sets of pertinent actions to perform on a meal in order to make it well balanced. The search is guided by an evaluation function based on fuzzy pattern matching indexes. The different versions of the algorithm have been benchmarked against a test database of real meals. Finally, the medical efficacy of Nutri-Expert and its acceptance by end-users have been demonstrated in several medical studies, the main results of which are presented. View full abstract»

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  • Self-organizing neuro-fuzzy system for control of unknown plants

    Publication Year: 2003 , Page(s): 135 - 150
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1102 KB) |  | HTML iconHTML  

    A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and the inference activities of the cluster-based SO-NFS. With the construction theory, a compact equation for describing the relation between the input base variables and inference results is established. This equation not only gives the inference relation between inputs and outputs but also specifies the linguistic meanings in the process. New pseudo-error learning control is proposed for closed-loop control applications. Using a cluster-based algorithm, the neuro-fuzzy system in its genesis can be generated by the stimulation of input/output training data to have its initial control policy (IF-THEN rules) for application. With the well-known random optimization method, the generated neuro-fuzzy system can learn its data base for specific applications. The proposed approach can be applied on control of unknown plants, and can levitate the curse of dimensionality in traditional fuzzy systems. Two examples are demonstrated. View full abstract»

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  • Qualitative reasoning based on fuzzy relative orders of magnitude

    Publication Year: 2003 , Page(s): 9 - 23
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1187 KB) |  | HTML iconHTML  

    This paper proposes a fuzzy set-based approach for handling relative orders of magnitude stated in terms of closeness and negligibility relations. At the semantic level, these relations are represented by means of fuzzy relations controlled by tolerance parameters. A set of sound inference rules, involving the tolerance parameters, is provided, in full accordance with the combination/projection principle underlying the approximate reasoning method of Zadeh. These rules ensure a local propagation of fuzzy closeness and negligibility relations. A numerical semantics is then attached to the symbolic computation process. Required properties of the tolerance parameter are investigated, in order to preserve the validity of the produced conclusions. The effect of the chaining of rules in the inference process can be controlled through the gradual deterioration of closeness and negligibility relations involved in the produced conclusions. Finally, qualitative reasoning based on fuzzy closeness and negligibility relations is used for simplifying equations and solving them in an approximate way, as often done by engineers who reason about a mathematical model. The problem of handling qualitative probabilities in reasoning under uncertainty is also investigated in this perspective. View full abstract»

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  • Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers

    Publication Year: 2003 , Page(s): 45 - 56
    Cited by:  Papers (113)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (932 KB)  

    In this paper, we present a new method for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Firstly, we present a method called the simple center of gravity method (SCGM) to calculate the center-of-gravity (COG) points of generalized fuzzy numbers. Then, we use the SCGM to propose a new method to measure the degree of similarity between generalized fuzzy numbers. The proposed similarity measure uses the SCGM to calculate the COG points of trapezoidal or triangular generalized fuzzy numbers and then to calculate the degree of similarity between generalized fuzzy numbers. We also prove some properties of the proposed similarity measure and use an example to compare the proposed method with the existing similarity measures. The proposed similarity measure can overcome the drawbacks of the existing methods. We also apply the proposed similarity measure to develop a new method to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis method is more flexible and more intelligent than the existing methods due to the fact that it considers the degrees of confidence of decisionmakers' opinions. View full abstract»

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  • A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems

    Publication Year: 2003 , Page(s): 24 - 34
    Cited by:  Papers (114)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (820 KB) |  | HTML iconHTML  

    A hybrid indirect and direct adaptive fuzzy output tracking control schemes are developed for a class of nonlinear multiple-input-multiple-output (MIMO) systems. This hybrid control system consists of observer and other different control components. Using the state observer, it does not require the system states to be available for measurement. Assisted by observer-based state feedback control component, the adaptive fuzzy system plays a dominant role to maintain the closed-loop stability. Being the auxiliary compensation, H control and sliding mode control are designed to suppress the influence of external disturbance and remove fuzzy approximation error, respectively. Thus, the system performance can be greatly improved. The simulation results demonstrate that the proposed hybrid fuzzy control system can guarantee the system stability and also maintain a good tracking performance. View full abstract»

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  • Stable adaptive fuzzy control of nonlinear systems preceded by unknown backlash-like hysteresis

    Publication Year: 2003 , Page(s): 1 - 8
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (740 KB) |  | HTML iconHTML  

    This paper deals with adaptive control of nonlinear dynamic systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is described by a dynamic equation. By utilizing this dynamic model and by combining a fuzzy universal function approximator with adaptive control techniques, a stable adaptive fuzzy control algorithm is developed without constructing a hysteresis inverse. The stability of the closed-loop system is shown using Lyapunov arguments. The effectiveness of the proposed method is demonstrated through simulations. View full abstract»

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  • Data-driven linguistic modeling using relational fuzzy rules

    Publication Year: 2003 , Page(s): 121 - 134
    Cited by:  Papers (29)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1041 KB) |  | HTML iconHTML  

    This paper presents a new approach to fuzzy rule-based modeling of nonlinear systems from numerical data. The novelty of the approach lies in the way of input partitioning and in the syntax of the rules. This paper introduces interpretable relational antecedents that incorporate local linear interactions between the input variables into the inference process. This modification improves the approximation quality and allows for limiting the number of rules. Additionally, the resulting linguistic description better captures the system characteristics by exposing the interactions between the input variables. View full abstract»

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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