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"
}