Messy genetic algorithm based new learning method for structurally optimized neurofuzzy controllers

M. Munir-ul, M. Chowdhury, Yun Li

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

9 Citations (Scopus)

Abstract

The success of a neurofuzzy control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. In a regular genetic algorithm, however, a difficulty exists which lies in the encoding of the problem by highly fit gene combinations of a fixed-length. For the structure of the controller to be coded, the required linkage format is not exactly known and the chance of obtaining such a linkage in a random generation of coded chromosomes is slim. This paper presents a new approach to structurally optimized designs of neurofuzzy controllers. Here, we use messy genetic algorithms, whose main characteristic is the variable length of chromosomes, to obtain structurally optimized FLC. Structural optimization is regarded important before neural network based local learning is switched into. The example of a cart-pole balancing problem demonstrates that such an optimal design realizes the potential of nonlinear proportional plus derivative type FLC in dealing with steady-state errors without the need of memberships or rule dimensions of an integral term.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Industrial Technology
Editors Anon
PublisherIEEE
Pages274-278
Number of pages5
DOIs
Publication statusPublished - 2 Dec 1996
EventProceedings of the IEEE International Conference on Industrial Technology - Shanghai, China
Duration: 5 Dec 19949 Dec 1994

Conference

ConferenceProceedings of the IEEE International Conference on Industrial Technology
CityShanghai, China
Period5/12/949/12/94

Keywords

  • fuzzy neural nets
  • neurocontrollers
  • fuzzy control
  • intelligent control
  • genetic algorithms
  • encoding
  • learning (artificial intelligence)
  • nonlinear control systems

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  • Cite this

    Munir-ul, M., Chowdhury, M., & Li, Y. (1996). Messy genetic algorithm based new learning method for structurally optimized neurofuzzy controllers. In Anon (Ed.), Proceedings of the IEEE International Conference on Industrial Technology (pp. 274-278). IEEE. https://doi.org/10.1109/ICIT.1996.601589