Publication detail
Genetic Algorithm Utilization in Fuzzy Regression Modelling
POKORNÝ, M. ŽELASKO, P. ROUPEC, J.
Czech title
Použití genetického algoritmu ve fuzzy regresním modelování
English title
Genetic Algorithm Utilization in Fuzzy Regression Modelling
Type
conference paper
Language
en
Original abstract
This paper introduces a soft-computing oriented approach to Takagi-Sugeno fuzzy modelling using the evolutionary principles. The presented algorithm allows determination of relevant input variables of fuzzy model from their potential candidates. Genetic algorithms 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 a shade zone of genes are used. To clarify the advantages of the proposed approaches the numerical example of modellin of fuzzy non-linear system is presented.
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 Takagi-Sugeno fuzzy modelling using the evolutionary principles. The presented algorithm allows determination of relevant input variables of fuzzy model from their potential candidates. Genetic algorithms 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 a shade zone of genes are used. To clarify the advantages of the proposed approaches the numerical example of modellin of fuzzy non-linear system is presented.
Keywords in Czech
Genetický algoritmnus; identifikace fuzzy modelu
Keywords in English
Genetic algorithm;fuzzy model identification
Released
29.08.2004
Location
Awaji, Japan
Book
Proceedings of Taiwan-Japan Symposium 2004 On Fuzzy Systems & Innovational Computing
Edition number
1
Pages count
8
BIBTEX
@inproceedings{BUT20755,
author="Miroslav {Pokorný} and Petr {Želasko} and Jan {Roupec},
title="Genetic Algorithm Utilization in Fuzzy Regression Modelling",
booktitle="Proceedings of Taiwan-Japan Symposium 2004 On Fuzzy Systems & Innovational Computing",
year="2004",
month="August",
address="Awaji, Japan"
}