Publication detail

Fuzzy Clustering Technology in Fuzzy Model Identification

POKORNÝ, M. ŽELASKO, P. ROUPEC, J.

Original Title

Fuzzy Clustering Technology in Fuzzy Model Identification

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.

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

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

Released

31.08.2004

Location

Japonsko

Pages from

168

Pages to

173

Pages count

6

BibTex


@inproceedings{BUT22575,
  author="Miroslav {Pokorný} and Petr {Želasko} and Jan {Roupec}",
  title="Fuzzy Clustering Technology in Fuzzy Model Identification",
  annote="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.",
  booktitle="Proceedings of 7th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty",
  chapter="22575",
  year="2004",
  month="august",
  pages="168",
  type="conference paper"
}