Publication detail

Genetic Algorithm Utilization in Fuzzy Regression Modelling

POKORNÝ, M. ŽELASKO, P. ROUPEC, J.

Original Title

Genetic Algorithm Utilization in Fuzzy Regression Modelling

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.

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

Genetic algorithm;fuzzy model identification

Released

29.08.2004

Location

Awaji, Japan

Pages from

154

Pages to

161

Pages count

8

BibTex


@inproceedings{BUT20755,
  author="Miroslav {Pokorný} and Petr {Želasko} and Jan {Roupec}",
  title="Genetic Algorithm Utilization in Fuzzy Regression Modelling",
  annote="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.",
  booktitle="Proceedings of Taiwan-Japan Symposium 2004 On Fuzzy Systems & Innovational Computing",
  chapter="20755",
  year="2004",
  month="august",
  pages="154",
  type="conference paper"
}