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Fuzzy associative matrix

About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.


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08 Feb 2007
TL;DR: In this article, the authors present a stability analysis of T-S Fuzzy Control Systems using a delay independent method and a delay dependent method for output tracking and fuzzy filter design.
Abstract: Stability Analysis of T-S Fuzzy Systems.- Extension to Fuzzy Large-Scale Systems.- Stabilization Methods for T-S Fuzzy Systems.- Variable Structure Control for T-S Fuzzy Systems.- Observer-Based Fuzzy Control: Delay-Independent Method.- Observer-Based Fuzzy Control: Delay-Dependent Method.- Output Tracking Control for T-S Fuzzy Systems.- Fuzzy Filter Design for T-S Fuzzy Systems.- Descriptor Method for T-S Fuzzy Control Systems.

122 citations

Journal ArticleDOI
TL;DR: In this article, the inverse problem concerned with fuzzy relations is investigated and conditions for the existence of a solution are shown and an analytical solution is given, and a method for the improvement of the solution is proposed.

122 citations

Book
18 Nov 1996
TL;DR: In this work, the semantic of fuzzy rule-languages is extended with a type system and an object-oriented system and the class of genetic algorithms over context-free languages has been developed.
Abstract: This work presents fuzzy rule-languages as links between quantitative and qualitative models. In the first part, the semantic of fuzzy rule-languages is extended with a type system and an object-oriented system. In the second part, fuzzy rule-languages are integrated with genetic algorithms and with classifier systems. For this purpose, the class of genetic algorithms over context-free languages has been developed.

121 citations

Journal ArticleDOI
TL;DR: The concept of a mapping on classes of fuzzySoft sets is defined and the properties of fuzzy soft images and fuzzy soft inverse images of fuzzysoft sets are studied.
Abstract: We define the concept of a mapping on classes of fuzzy soft sets and study the properties of fuzzy soft images and fuzzy soft inverse images of fuzzy soft sets, and support them with examples and counterexamples.

121 citations

Journal ArticleDOI
TL;DR: This paper describes a simple fuzzy classifiers system where a randomly generated initial population of fuzzy if-then rules is evolved by typical genetic operations, such as selection, crossover, and mutation, and introduces two heuristic procedures for improving the performance of the fuzzy classifier system.
Abstract: In this paper, various methods are introduced for improving the ability of fuzzy classifier systems to automatically generate fuzzy if-then rules for pattern classification problems with continuous attributes. First, we describe a simple fuzzy classifier system where a randomly generated initial population of fuzzy if-then rules is evolved by typical genetic operations, such as selection, crossover, and mutation. By computer simulations on a real-world pattern classification problem with many continuous attributes, we show that the search ability of such a simple fuzzy classifier system is not high. Next, we examine the search ability of a hybrid algorithm where a learning procedure of fuzzy if-then rules is combined with the fuzzy classifier system. Then, we introduce two heuristic procedures for improving the performance of the fuzzy classifier system. One is a heuristic rule generation procedure for an initial population where initial fuzzy if-then rules are directly generated from training patterns. The other is a heuristic population update procedure where new fuzzy if-then rules are generated from misclassified and rejected training patterns, as well as from existing fuzzy if-then rules by genetic operations. By computer simulations, we demonstrate that these two heuristic procedures drastically improve the search ability of the fuzzy classifier system. We also examine a variant of the fuzzy classifier system where the population size (i.e., the number of fuzzy if-then rules) varies depending on the classification performance of fuzzy if-then rules in the current population.

121 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202216
20212
20201
20193
201825