Topic
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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TL;DR: It is argued that this technique can produce good approximate solutions by applying it to solve a fuzzy optimization problem by introducing fuzzy genetic algorithms to (approximately) solve fuzzy optimization problems.
102 citations
TL;DR: A comprehensive study of fuzzy geometry is introduced by first defining a fuzzy point and a fuzzy line in fuzzy plane geometry and showing it is a (weak) fuzzy metric.
102 citations
TL;DR: A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed, and results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules.
101 citations
10 Dec 1997
TL;DR: LMI based design procedures for fuzzy regulators and fuzzy observers are constructed using the parallel distributed compensation and the relaxed stability conditions to realize effective and practical designs by utilizing other LMIs with respect to decay rate and constraints on control input and output.
Abstract: This paper presents LMI (linear matrix inequality) based designs of fuzzy control systems based on new relaxed stability conditions. LMI based design procedures for fuzzy regulators and fuzzy observers are constructed using the parallel distributed compensation and the relaxed stability conditions. The design procedures realize effective and practical designs by utilizing other LMIs with respect to decay rate and constraints on control input and output. A design example for a nonlinear system demonstrates the utility of the LMI based design procedures.
101 citations
01 Dec 1999
TL;DR: A hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available.
Abstract: In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach.
101 citations