# IEEE Transactions on Fuzzy Systems

## Filter Results

Displaying Results 1 - 19 of 19

Publication Year: 2012, Page(s): C1
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• ### IEEE Transactions on Fuzzy Systems publication information

Publication Year: 2012, Page(s): C2
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• ### Finite-Time $H_{infty}$ Fuzzy Control of Nonlinear Jump Systems With Time Delays Via Dynamic Observer-Based State Feedback

Publication Year: 2012, Page(s):605 - 614
Cited by:  Papers (111)
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This paper studies the finite-time H∞ control problem for time-delay nonlinear jump systems via dynamic observer-based state feedback by the fuzzy Lyapunov-Krasovskii functional approach. The Takagi-Sugeno (T-S) fuzzy model is first employed to represent the presented nonlinear Markov jump systems (MJSs) with time delays. Based on the selected Lyapunov-Krasovskii functiona... View full abstract»

• ### A Practical Approach to R&D Portfolio Selection Using the Fuzzy Pay-Off Method

Publication Year: 2012, Page(s):615 - 622
Cited by:  Papers (14)
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The objective of this research is to develop a practical research and development (R&D) portfolio selection model that addresses the effective R&D project valuation issue, while tackling R&D uncertainty in portfolio optimization. Fuzzy set theory is employed to capture and model the uncertain project information. To evade the well-known complexities of fuzzy real option valuation, the ... View full abstract»

• ### Fuzzy Hardware: A Retrospective and Analysis

Publication Year: 2012, Page(s):623 - 635
Cited by:  Papers (13)
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Worldwide, fuzzy systems are becoming a very useful mathematical tool to deal with nonlinear problems by inferring from a rule base that contains the necessary knowledge extracted from an expert. There are diverse forms to implement a fuzzy system depending on the desired response time, for which hardware implementations are best suited for high-speed demands. Fuzzy hardware has been exploited sin... View full abstract»

• ### On Robust Fuzzy Rough Set Models

Publication Year: 2012, Page(s):636 - 651
Cited by:  Papers (36)
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Rough sets, especially fuzzy rough sets, are supposedly a powerful mathematical tool to deal with uncertainty in data analysis. This theory has been applied to feature selection, dimensionality reduction, and rule learning. However, it is pointed out that the classical model of fuzzy rough sets is sensitive to noisy information, which is considered as a main source of uncertainty in applications. ... View full abstract»

• ### Fault-Tolerant Control for T–S Fuzzy Systems With Application to Near-Space Hypersonic Vehicle With Actuator Faults

Publication Year: 2012, Page(s):652 - 665
Cited by:  Papers (71)
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This paper addresses the problem of fault-tolerant control for Takagi-Sugeno (T-S) fuzzy systems with actuator faults. First, a general actuator fault model is proposed, which integrates time-varying bias faults and time-varying gain faults. Then, sliding-mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel... View full abstract»

• ### Constrained Fuzzy Hierarchical Analysis for Portfolio Selection Under Higher Moments

Publication Year: 2012, Page(s):666 - 682
Cited by:  Papers (9)
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Marginal impacts of assets on portfolio higher moments are characterized by triangular fuzzy numbers and then evaluated by fuzzy ranking procedures in order to assemble fuzzy reciprocal matrices that are needed for the constrained fuzzy analytic hierarchy process (AHP) methods. The proposed methodology increases the scope for emphasizing objective quantitative measures, thus alleviating the influe... View full abstract»

• ### An Integrated Mechanism for Feature Selection and Fuzzy Rule Extraction for Classification

Publication Year: 2012, Page(s):683 - 698
Cited by:  Papers (20)
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In our view, the most important characteristic of a fuzzy rule-based system is its readability, which is seriously affected by, among other things, the number of features used to design the rule base. Hence, for high-dimensional data, dimensionality reduction through feature selection (not extraction) is very important. Our objective, here, is not to find an optimal rule base for classification bu... View full abstract»

• ### Generalizing the Decentralized Control of Fuzzy Discrete Event Systems

Publication Year: 2012, Page(s):699 - 714
Cited by:  Papers (4)
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The main objective of this paper is to establish a general architecture for decentralized supervision of fuzzy discrete event systems (FDES). First, two different types of decentralized supervisory control architectures of FDES are presented, which fuse the locally enabled degrees of fuzzy events using the fuzzy-intersection operator and the fuzzy-union operator, respectively. Both of these archit... View full abstract»

• ### Participatory Learning of Propositional Knowledge

Publication Year: 2012, Page(s):715 - 727
Cited by:  Papers (5)
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Our objective here is to extend the participatory learning paradigm (PLP) to environments in which we are interested in learning information and knowledge expressed in terms of declarative statements. We first recall the basic idea of participatory learning, which stresses the important role of what is already believed in all aspects of the learning process. We then discuss the representation of d... View full abstract»

• ### The $K$-Means-Type Algorithms Versus Imbalanced Data Distributions

Publication Year: 2012, Page(s):728 - 745
Cited by:  Papers (14)
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K-means is a partitional clustering technique that is well-known and widely used for its low computational cost. The representative algorithms include the hard k-means and the fuzzy k -means. However, the performance of these algorithms tends to be affected by skewed data distributions, i.e., imbalanced data. They often produce clusters of relatively uniform sizes, even if inp... View full abstract»

• ### Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals

Publication Year: 2012, Page(s):746 - 759
Cited by:  Papers (11)
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Quantifying stress levels of an individual based on a mathematical analysis of real-time physiological data measurements is challenging. This study suggests a stochastic fuzzy analysis method to evaluate the short time series of R-R intervals (time intervals between consecutive heart beats) for a quantification of the stress level. The 5-min-long series of R-R intervals recorded under a given stre... View full abstract»

• ### Fuzzy Preferences in the Graph Model for Conflict Resolution

Publication Year: 2012, Page(s):760 - 770
Cited by:  Papers (28)
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A new framework for the graph model for conflict resolution is developed so that decision makers (DMs) with fuzzy preferences can be included in conflict models. A graph model is both a formal representation for multiple participant-multiple objective decision problems and a set of analysis procedures that add insights into them. Within the new framework, graph models can include-and integrate int... View full abstract»

• ### Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems

Publication Year: 2012, Page(s):771 - 785
Cited by:  Papers (153)
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This paper is concerned with the problem of adaptive fuzzy tracking control for a class of uncertain multiple-input-multiple-output (MIMO) pure-feedback nonlinear systems with immeasurable states. The dynamic output feedback strategy begins with a state observer. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions. The filtered signals are introduced to circumvent algeb... View full abstract»

• ### On the Use of a Fuzzy Object-Relational Database for Flexible Retrieval of Medical Images

Publication Year: 2012, Page(s):786 - 803
Cited by:  Papers (4)
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This paper introduces a novel approach to medical image retrieval using a fuzzy object-relational database management system (FORDBMS). The system stores medical images along with information about the content of the image, such as the presence or absence of certain indicators of pathologies. It allows us to flexibly retrieve them on the basis of these indicators, making it possible to obtain imag... View full abstract»

• ### IEEE Xplore Digital Library [advertisement]

Publication Year: 2012, Page(s): 804
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• ### IEEE Computational Intelligence Society Information

Publication Year: 2012, Page(s): C3
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• ### IEEE Transactions on Fuzzy Systems information for authors

Publication Year: 2012, 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