Detail publikace

Fuzzy Clustering Technology in Fuzzy Model Identification

POKORNÝ, M. ŽELASKO, P. ROUPEC, J.

Anglický název

Fuzzy Clustering Technology in Fuzzy Model Identification

Typ

Stať ve sborníku v databázi WoS či Scopus

Jazyk

en

Originální abstrakt

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.

Klíčová slova anglicky

Takagi-Sugeno fuzzy model;input variables selection;fuzzy clustering;advanced genetic algorithm;numerical example

Vydáno

2004-08-31

Místo

Japonsko

Kniha

Proceedings of 7th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty

Strany od–do

168–

Počet stran

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",
  pages="6",
  address="Japonsko"
}