Publication detail

Genetic Algorithm Utilization in Fuzzy Regression Modelling

POKORNÝ, M. ŽELASKO, P. ROUPEC, J.

English title

Genetic Algorithm Utilization in Fuzzy Regression Modelling

Type

Paper in proceedings (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.

Keywords in English

Genetic algorithm;fuzzy model identification

Released

2004-08-29

Location

Awaji, Japan

Book

Proceedings of Taiwan-Japan Symposium 2004 On Fuzzy Systems & Innovational Computing

Pages from–to

154–

Pages count

8

BIBTEX


@inproceedings{BUT20755,
  author="Miroslav {Pokorný} and Petr {Želasko} and Jan {Roupec}",
  title="Genetic Algorithm Utilization in Fuzzy Regression Modelling",
  booktitle="Proceedings of Taiwan-Japan Symposium 2004 On Fuzzy Systems & Innovational Computing",
  year="2004",
  number="1",
  pages="8",
  address="Awaji, Japan"
}