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# IEEE Transactions on Fuzzy Systems

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Displaying Results 1 - 22 of 22

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 (10)
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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 in... View full abstract»

• ### 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 (58)
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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-del... View full abstract»

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

Publication Year: 2011, Page(s):636 - 651
Cited by:  Papers (19)
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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... View full abstract»

• ### 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 (37)
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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 computatio... View full abstract»

• ### 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 (73)
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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 ... View full abstract»

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

Publication Year: 2011, Page(s):682 - 693
Cited by:  Papers (24)
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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 contrib... View full abstract»

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

Publication Year: 2011, Page(s):694 - 706
Cited by:  Papers (46)
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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,... View full abstract»

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

Publication Year: 2011, Page(s):707 - 716
Cited by:  Papers (18)
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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 ... View full abstract»

• ### 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 (50)
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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 admirab... View full abstract»

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

Publication Year: 2011, Page(s):729 - 744
Cited by:  Papers (32)
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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 functio... View full abstract»

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

Publication Year: 2011, Page(s):745 - 757
Cited by:  Papers (90)
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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,... View full abstract»

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

Publication Year: 2011, Page(s):758 - 769
Cited by:  Papers (30)
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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... View full abstract»

• ### 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 (29)
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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 L... View full abstract»

• ### Fuzzy-Zoning-Based Classification for Handwritten Characters

Publication Year: 2011, Page(s):780 - 785
Cited by:  Papers (17)
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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 classificat... View full abstract»

• ### 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 (59)
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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 m... View full abstract»

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

Publication Year: 2011, Page(s):792 - 794
Cited by:  Papers (12)
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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 dataset... View full abstract»

• ### IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

Publication Year: 2011, Page(s): 795
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Publication Year: 2011, Page(s): 796
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• ### IEEE Computational Intelligence Society Information

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

Publication Year: 2011, Page(s): C4
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## Aims & Scope

The IEEE Transactions on Fuzzy Systems (TFS) is published bimonthly. TFS will consider papers that deal with the theory, design or an application of fuzzy systems ranging from hardware to software.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Jonathan Garibaldi
University of Nottingham
Nottingham NG8 1BB, U.K.
jon.garibaldi@nottingham.ac.uk
Phone: +44 115 95 14216
Fax: +44 115 95 14799