Detail publikace

Fuzzy Clustering Technology in Fuzzy Model Identification

POKORNÝ, M. ŽELASKO, P. ROUPEC, J.

Originální název

Fuzzy Clustering Technology in Fuzzy Model Identification

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

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

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

Autoři

POKORNÝ, M.; ŽELASKO, P.; ROUPEC, J.

Vydáno

31. 8. 2004

Místo

Japonsko

Strany od

168

Strany do

173

Strany počet

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"
}