Publication detail
Fuzzy Clustering Technology in Fuzzy Model Identification
POKORNÝ, M. ŽELASKO, P. ROUPEC, J.
Czech title
Použití fuzzy shlukovací metody v procesu identifikace fuzzy modelu
English title
Fuzzy Clustering Technology in Fuzzy Model Identification
Type
conference paper
Language
en
Original abstract
This paper introduces a soft-computing oriented approach to Takgi-Sugeno fuzzy modelling using the evolutionary principles. Genetic algorithm are applied to optimize fuzzy input variables space through genetic fuzzy clustering procedure and to identify the fuzzy model. Some advanced procedures e.g. individuals lifetime limitation and redundant genes application are used. The presented algorithm allows also the determination of the relevant inpus variables of fuzzy model from theirs potential candidates.To clarify the advantages of the proposed approaches the numerical example of modelling of fuzzy non-linear system is also introduced.
Czech abstract
Referát uvádí přístup Takagi-Sugenova soft-computingového fuzzy modelování s použitím evolučních přístupů. Pro optimalizaci diverzifikace vstupního fuzzy prostoru a identifikaci modelu je použita fuzzy-genetická shlukovací procedura. Použitý genetický algoritmus využívá některé moderní operátory jako limitaci doby životnosti jedince v populaci nebo bitové redundance v chromozomech. Uvedený algoritmus umožňuje rovněž stanovit redundantní vstupní proměnné fuzzy modelu. Pro ověření kvality funkce navrženého algoritmu je uveden numerický příklad modelování nelineární soustavy.
English abstract
This paper introduces a soft-computing oriented approach to Takgi-Sugeno fuzzy modelling using the evolutionary principles. Genetic algorithm are applied to optimize fuzzy input variables space through genetic fuzzy clustering procedure and to identify the fuzzy model. Some advanced procedures e.g. individuals lifetime limitation and redundant genes application are used. The presented algorithm allows also the determination of the relevant inpus variables of fuzzy model from theirs potential candidates.To clarify the advantages of the proposed approaches the numerical example of modelling of fuzzy non-linear system is also introduced.
Keywords in Czech
Takagi-Sugeno fuzzy model; selekce vstupních proměnných;pokročilý genetický algoritmus;numerický příklad
Keywords in English
Takagi-Sugeno fuzzy model;input variables selection;fuzzy clustering;advanced genetic algorithm;numerical example
Released
31.08.2004
Location
Japonsko
Book
Proceedings of 7th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty
Pages count
6
BIBTEX
@inproceedings{BUT22575,
author="Miroslav {Pokorný} and Petr {Želasko} and Jan {Roupec},
title="Fuzzy Clustering Technology in Fuzzy Model Identification",
booktitle="Proceedings of 7th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty",
year="2004",
month="August",
address="Japonsko"
}