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Fuzzy Systems, IEEE Transactions on

Issue 4 • Date Aug. 2011

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Displaying Results 1 - 22 of 22
  • 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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  • Measuring Inconsistency in Fuzzy Answer Set Semantics

    Publication Year: 2011 , Page(s): 605 - 622
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (457 KB) |  | HTML iconHTML  

    Recent approaches have shown that the measurement of the amount of inconsistent information contained in a logic theory can be useful to infer positive information. This paper deals with the definition of measures of inconsistency in the residuated-logic-programming paradigm under the fuzzy answer set semantics. This fuzzy framework provides a soft mechanism to control the amount of information inferred and, thus, controlling the inconsistencies by modifying slightly the truth values of some rules. View full abstract»

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  • Chaos Synchronization of Uncertain Fractional-Order Chaotic Systems With Time Delay Based on Adaptive Fuzzy Sliding Mode Control

    Publication Year: 2011 , Page(s): 623 - 635
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2209 KB) |  | HTML iconHTML  

    This paper proposes an adaptive fuzzy sliding mode control (AFSMC) to synchronize two different uncertain fractional-order time-delay chaotic systems, which are infinite dimensional in nature, and time delay is a source of instability. Because modeling the behavior of dynamical systems by fractional-order differential equations has more advantages than integer-order modeling, the adaptive time-delay fuzzy-logic system is constructed to approximate the unknown fractional-order time-delay-system functions. By using Lyapunov stability criterion, the free parameters of the adaptive fuzzy controller can be tuned online by output-feedback-control law and adaptive law. The sliding mode design procedure not only guarantees the stability and robustness of the proposed AFSMC, but it also guarantees that the external disturbance on the synchronization error can be attenuated. The simulation example is included to confirm validity and synchronization performance of the advocated design methodology. View full abstract»

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  • A New Prediction Model Based on Belief Rule Base for System's Behavior Prediction

    Publication Year: 2011 , Page(s): 636 - 651
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1279 KB) |  | HTML iconHTML  

    In engineering practice, a system's behavior constantly changes over time. To predict the behavior of a complex engineering system, a model can be built and trained using historical data. This paper addresses the forecasting problems with a belief rule base (BRB) to trace and predict system performance in a more interpretable and transparent way. More precisely, it extends the BRB method to handle a system's behavior prediction, and a new prediction model based on BRB is presented, which can model and analyze prediction problems using not only numerical data but human judgmental information as well. The proposed forecasting model includes some unknown parameters that can be manually tuned and trained. To build an effective BRB forecasting model, a multiple-objective optimization model is provided to locally train the BRB prediction model by minimizing the mean square error (MSE). Finally, a practical case study is provided to illustrate the detailed implementation procedures and examine the feasibility of the proposed approach in engineering application. Furthermore, the comparative studies with other state-of-the-art prediction methods are carried out. It is shown that the proposed model is effective and can generate better prediction in terms of accuracy, as well as comprehensibility. View full abstract»

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  • Connect Karnik-Mendel Algorithms to Root-Finding for Computing the Centroid of an Interval Type-2 Fuzzy Set

    Publication Year: 2011 , Page(s): 652 - 665
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (626 KB) |  | HTML iconHTML  

    Based on a new continuous Karnik-Mendel (KM) algorithm expression, this paper proves that the centroid computation of an interval type-2 fuzzy set using KM algorithms is equivalent to the Newton-Raphson method in root-finding, which reveals the mechanisms in KM algorithm computation. The theoretical results of KM algorithms are re-obtained. Different from current KM algorithms, centroid computation methods that use different root-finding routines are provided. Such centroid computation methods can obtain the exact solution and are different from the current approximate methods using sampled data. Further improvements and analysis of the centroid problem using root-finding and integral computation techniques are also possible. View full abstract»

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  • A Fast and Scalable Multiobjective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems

    Publication Year: 2011 , Page(s): 666 - 681
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1083 KB) |  | HTML iconHTML  

    Linguistic fuzzy modeling in high-dimensional regression problems poses the challenge of exponential-rule explosion when the number of variables and/or instances becomes high. One way to address this problem is by determining the used variables, the linguistic partitioning and the rule set together, in order to only evolve very simple, but still accurate models. However, evolving these components together is a difficult task, which involves a complex search space. In this study, we propose an effective multiobjective evolutionary algorithm that, based on embedded genetic database (DB) learning (involved variables, granularities, and slight fuzzy-partition displacements), allows the fast learning of simple and quite-accurate linguistic models. Some efficient mechanisms have been designed to ensure a very fast, but not premature, convergence in problems with a high number of variables. Further, since additional problems could arise for datasets with a large number of instances, we also propose a general mechanism for the estimation of the model error when using evolutionary algorithms, by only considering a reduced subset of the examples. By doing so, we can also apply a fast postprocessing stage for further refining the learned solutions. We tested our approach on 17 real-world datasets with different numbers of variables and instances. Three well-known methods based on embedded genetic DB learning have been executed as references. We compared the different approaches by applying nonparametric statistical tests for multiple comparisons. The results confirm the effectiveness of the proposed method not only in terms of scalability but in terms of the simplicity and generalizability of the obtained models as well. View full abstract»

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  • Extraction and Adaptation of Fuzzy Rules for Friction Modeling and Control Compensation

    Publication Year: 2011 , Page(s): 682 - 693
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (902 KB) |  | HTML iconHTML  

    Modeling of friction forces has been a challenging task in mechanical engineering. Parameterized approaches for modeling friction find it difficult to achieve satisfactory performance due to the presence of nonlinearity and uncertainties in dynamical systems. This paper aims to develop adaptive fuzzy friction models by the use of data-mining techniques and system theory. Our main technical contributions are twofold: extraction of fuzzy rules and formulation of a static fuzzy friction model and adaptation of the fuzzy friction model by the use of the Lyapunov stability theory, which is associated with a control compensation of a typical motion dynamics. The proposed framework in this paper shows a successful application of adaptive data-mining techniques in engineering. A single-degree-of-freedom mechanical system is employed as an experimental model in simulation studies. Results demonstrate that our proposed fuzzy friction model has promise in the design of uncertain mechanical control systems. View full abstract»

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  • Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems

    Publication Year: 2011 , Page(s): 694 - 706
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1291 KB) |  | HTML iconHTML  

    This paper proposes a robust self-organizing neural-fuzzy-control (RSONFC) scheme for a class of uncertain nonlinear multiple-input-multiple-output (MIMO) systems. We first develop a self-organizing neural-fuzzy network (SONFN) with concurrent structure and parameter learning. The fuzzy rules of SONFN are generated or pruned systematically. The proposed RSONFC scheme comprises an SONFN identifier, an uncertainty observer, and a supervisory controller. The SONFN identifier functions as the principal controller, and the uncertainty observer is designed to oversee uncertainties within the compound system. The supervisory controller combines sliding-mode control (SMC) and an adaptive bound-estimation scheme with various weights to achieve H tracking performance with a desired level of attenuation. Projection-type adaptation laws of network parameters developed using the Lyapunov's synthesis approach guarantee the stability of the overall control system. Simulation studies on a single-link flexible-joint manipulator and a two-link robot demonstrate the effectiveness of the proposed control scheme. View full abstract»

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  • Piecewise Sliding-Mode Control for T–S Fuzzy Systems

    Publication Year: 2011 , Page(s): 707 - 716
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (858 KB) |  | HTML iconHTML  

    This paper addresses piecewise sliding-mode control for Takagi-Sugeno (T-S) fuzzy models. A novel sliding-mode control (SMC) design approach is developed which is based on individual sliding surface in each local region of the T-S fuzzy systems. Conditions of existence of sliding mode in the associated region are given. The chattering effect around region boundaries is analyzed, and prevention of such chattering is discussed. Two illustrative examples are finally given to illustrate the effectiveness and performance of the proposed controller. View full abstract»

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  • Speedup of Implementing Fuzzy Neural Networks With High-Dimensional Inputs Through Parallel Processing on Graphic Processing Units

    Publication Year: 2011 , Page(s): 717 - 728
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1409 KB) |  | HTML iconHTML  

    This paper proposes the implementation of a zero-order Takagi-Sugeno-Kang (TSK)-type fuzzy neural network (FNN) on graphic processing units (GPUs) to reduce training time. The software platform that this study uses is the compute unified device architecture (CUDA). The implemented FNN uses structure and parameter learning in a self-constructing neural fuzzy inference network because of its admirable learning performance. FNN training is conventionally implemented on a single-threaded CPU, where each input variable and fuzzy rule is serially processed. This type of training is time consuming, especially for a high-dimensional FNN that consists of a large number of rules. The GPU is capable of running a large number of threads in parallel. In a GPU-implemented FNN (GPU-FNN), blocks of threads are partitioned according to parallel and independent properties of fuzzy rules. Large sets of input data are mapped to parallel threads in each block. For memory management, this research suitably divides the datasets in the GPU-FNN into smaller chunks according to fuzzy rule structures to share on-chip memory among multiple thread processors. This study applies the GPU-FNN to different problems to verify its efficiency. The results show that to train an FNN with GPU implementation achieves a speedup of more than 30 times that of CPU implementation for problems with high-dimensional attributes. View full abstract»

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  • Weighted Fuzzy Rule Interpolation Based on GA-Based Weight-Learning Techniques

    Publication Year: 2011 , Page(s): 729 - 744
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1208 KB) |  | HTML iconHTML  

    In this paper, we propose a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. It is based on a genetic algorithm (GA)-based weight-learning technique. The proposed method can deal with fuzzy rule interpolation with weighted antecedent variables. It also can deal with fuzzy rule interpolation based on polygonal membership functions and bell-shaped membership functions. We also propose a GA-based weight-learning algorithm to automatically learn the optimal weights of the antecedent variables of the fuzzy rules. Furthermore, we apply the proposed weighted fuzzy interpolative reasoning method and the proposed GA-based weight-learning algorithm to deal with the truck backer-upper control problem, the computer activity prediction problem, multivariate regression problems, and time series prediction problems. Based on statistical analysis techniques, the experimental results show that the proposed weighted fuzzy interpolative reasoning method by the use of the optimally learned weights that were obtained by the proposed GA-based weight-learning algorithm has statistically significantly smaller error rates than the existing methods. View full abstract»

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  • Adaptive Fuzzy Control for Synchronization of Nonlinear Teleoperators With Stochastic Time-Varying Communication Delays

    Publication Year: 2011 , Page(s): 745 - 757
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (843 KB) |  | HTML iconHTML  

    In this paper, adaptive fuzzy control is investigated for nonlinear teleoperators with time delays, which ensures synchronization of positions and velocities of the master and slave manipulators and does not rely on the use of the scattering transformation. Compared with the previous passivity framework, the communication delays are assumed to be stochastic time varying. By feedback linearization, the nonlinear dynamics of the teleoperation system is transformed into two subsystems: local master/slave position control with unmodeled dynamics and delayed motion synchronization. Then, based on linear matrix inequalities (LMI) and Markov jump linear systems, adaptive fuzzy-control strategies are developed for the nonlinear teleoperators with modeling uncertainties and external disturbances by using the approximation property of the fuzzy logic systems. It is proven that the master-slave teleoperation system is stochastically stable in mean square under specific LMI conditions, and all the signals of the resulting closed-loop system are uniformly bounded. Finally, the extensive simulations are performed to show the effectiveness of the proposed method. View full abstract»

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  • Fuzzy-Portfolio-Selection Models With Value-at-Risk

    Publication Year: 2011 , Page(s): 758 - 769
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (771 KB) |  | HTML iconHTML  

    Based on fuzzy value-at-risk (VaR), this paper proposes a new portfolio-selection model (PSM) called the VaR-based fuzzy PSM (VaR-FPSM). Compared with the existing FPSMs, the VaR can directly reflect the greatest loss of a selected case under a given confidence level. In this study, when the security returns are taken as trapezoidal, triangular, and Gaussian fuzzy numbers, several crisp equivalent models of the VaR-FPSM are derived, which can be handled by any linear programming solvers. In general situations, an improved particle swarm optimization algorithm on the basis of fuzzy simulation is designed to search for the approximate optimal solutions. To illustrate the proposed model and the behavior of the improved particle swarm optimization algorithm, two numerical examples are provided, and the results are discussed. Furthermore, the proposed algorithm is compared with some existing approaches to fuzzy portfolio selection, such as the genetic algorithm and simulated annealing. View full abstract»

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  • New Results on a Delay-Derivative-Dependent Fuzzy H ^\infty Filter Design for T–S Fuzzy Systems

    Publication Year: 2011 , Page(s): 770 - 779
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (647 KB) |  | HTML iconHTML  

    This paper focuses on the fuzzy-H-filter-design problem for Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delays. Two cases of the time-varying delays are considered: 1) The delays are differentiable and have both the lower and upper bounds of the delay derivatives, and 2) the delays are bounded but not necessary to be differentiable. Since we employ a new fuzzy Lyapunov-Krasovskii functional (LKF) and estimate a tighter upper bound of its derivative, the proposed delay-derivative-dependent bound-real-lemma (BRL) condition has advantages over some previous results in that it enlarges the application scope but has less conservatism, which is established theoretically. The BRL condition that depends on both the upper and lower bounds of the delay derivatives is derived. Then, based on the aforesaid BRL, a new fuzzy H filter scheme is proposed, and a sufficient condition for the existence of such a filter is established in terms of linear-matrix inequalities (LMIs). Finally, some numerical examples and an application to the truck-trailer system are utilized to demonstrate the effectiveness and reduced conservatism of our results. View full abstract»

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  • Fuzzy-Zoning-Based Classification for Handwritten Characters

    Publication Year: 2011 , Page(s): 780 - 785
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (792 KB) |  | HTML iconHTML  

    In zoning-based classification, a membership function defines the way a feature influences the different zones of the zoning method. This paper presents a new class of membership functions, which are called fuzzy-membership functions (FMFs), for zoning-based classification. These FMFs can be easily adapted to the specific characteristics of a classification problem in order to maximize classification performance. In this research, a real-coded genetic algorithm is presented to find, in a single optimization procedure, the optimal FMF, together with the optimal zoning described by Voronoi tessellation. The experimental results, which are carried out in the field of handwritten digit and character recognition, indicate that optimal FMF performs better than other membership functions based on abstract-level, ranked-level, and measurement-level weighting models, which can be found in the literature. View full abstract»

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  • A New Fuzzy Lyapunov Function for Relaxed Stability Condition of Continuous-Time Takagi–Sugeno Fuzzy Systems

    Publication Year: 2011 , Page(s): 785 - 791
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB) |  | HTML iconHTML  

    This paper presents a new fuzzy Lyapunov function (FLF) for the stability analysis of continuous-time Takagi-Sugeno (T-S) fuzzy systems. Unlike conventional FLFs, the proposed one depends not only on the fuzzy weighting functions of the T-S fuzzy systems but on their first-order time derivatives as well. Based on the proposed FLF, a sufficient stability condition is derived in the form of linear matrix inequalities, depending on the upper bounds on the second-order time derivative of the fuzzy weighting functions, as well as the first-order ones. Finally, some examples demonstrate that the proposed condition can provide less conservative results than the previous ones in the literature. View full abstract»

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  • Convergence of the Single-Pass and Online Fuzzy C-Means Algorithms

    Publication Year: 2011 , Page(s): 792 - 794
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (162 KB) |  | HTML iconHTML  

    Scalable versions of the widely used fuzzy c-means clustering algorithm called single-pass fuzzy c-means and online fuzzy c-means have been recently introduced. Both algorithms facilitate scaling to very large numbers of examples while providing partitions that very closely approximate those one would obtain using fuzzy c-means. Both algorithms have been successfully applied to a number of datasets, most notably, magnetic resonance image volumes of the human brain. In this letter, we show that weighting examples in the fuzzy c-means algorithm does not cause a violation in its convergence proof, and we provide a separate proof of convergence that holds for any dataset. View full abstract»

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  • IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

    Publication Year: 2011 , Page(s): 795
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    Freely Available from IEEE
  • IEEE Foundation [advertisement]

    Publication Year: 2011 , Page(s): 796
    Save to Project icon | Request Permissions | PDF file iconPDF (320 KB)  
    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Publication Year: 2011 , Page(s): C3
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    Freely Available from IEEE
  • IEEE Transactions on Fuzzy Systems Information for authors

    Publication Year: 2011 , Page(s): C4
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    Freely Available from IEEE

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