Neural networks and genetic algorithm approaches to auto-design of fuzzy systems

Hideyuki Takagi, Michael Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This paper presents Neural Network and Genetic Algorithm approaches to fuzzy system design, which aims to shorten development time and increase system performance. An approach that uses neural network to represent multi-dimensional nonlinear membership functions and an approach to tune membership function parameters are given. A genetic algorithm approach that integrates and automates three fuzzy system design stages is also proposed.

Original languageEnglish
Title of host publicationFuzzy Logic in Artificial Intelligence - 8th Austrian Artificial Intelligence Conference, FLAI 1993, Proceedings
EditorsErich P. Klement, Wolfgang Slany
PublisherSpringer Verlag
Pages68-79
Number of pages12
ISBN (Print)9783540569206
Publication statusPublished - Jan 1 1993
Externally publishedYes
Event8th Austrian Artificial Intelligence Conference on Fuzzy Logic, FLAI 1993 - Linz, Austria
Duration: Jun 28 1993Jun 30 1993

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume695 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th Austrian Artificial Intelligence Conference on Fuzzy Logic, FLAI 1993
CountryAustria
CityLinz
Period6/28/936/30/93

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Takagi, H., & Lee, M. (1993). Neural networks and genetic algorithm approaches to auto-design of fuzzy systems. In E. P. Klement, & W. Slany (Eds.), Fuzzy Logic in Artificial Intelligence - 8th Austrian Artificial Intelligence Conference, FLAI 1993, Proceedings (pp. 68-79). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 695 LNAI). Springer Verlag.