Fuzzy rule extraction by bacterial memetic algorithms

J. Botzheim, C. Cabrita, L. Kóczy, A. E. Ruano

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

In our previous papers, fuzzy model identification methods were discussed. The bacterial evolutionary algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of the evolutionary and the gradient-based learning techniques is usually called memetic algorithm. In this paper, a new kind of memetic algorithm, the bacterial memetic algorithm., is introduced for fuzzy rule extraction. The paper presents how the bacterial evolutionary algorithm can be improved with the Levenberg-Marquardt technique.

Original languageEnglish
Pages (from-to)312-339
Number of pages28
JournalInternational Journal of Intelligent Systems
Volume24
Issue number3
DOIs
Publication statusPublished - Mar 2009

Fingerprint

Rule Extraction
Memetic Algorithm
Fuzzy rules
Fuzzy Rules
Evolutionary algorithms
Evolutionary Algorithms
Fuzzy Identification
Levenberg-Marquardt Method
Fuzzy Rule Base
Model Identification
Levenberg-Marquardt
Fuzzy systems
Membership functions
Fuzzy Model
Membership Function
Fuzzy Systems
Identification (control systems)
Gradient

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Human-Computer Interaction
  • Theoretical Computer Science

Cite this

Fuzzy rule extraction by bacterial memetic algorithms. / Botzheim, J.; Cabrita, C.; Kóczy, L.; Ruano, A. E.

In: International Journal of Intelligent Systems, Vol. 24, No. 3, 03.2009, p. 312-339.

Research output: Contribution to journalArticle

Botzheim, J. ; Cabrita, C. ; Kóczy, L. ; Ruano, A. E. / Fuzzy rule extraction by bacterial memetic algorithms. In: International Journal of Intelligent Systems. 2009 ; Vol. 24, No. 3. pp. 312-339.
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