Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling

Ho Jae Lee, Jin Bae Park, Young Hoon Joo

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In constructing a successful fuzzy model for a complex chaotic system, identification of its constituent parameters is an important yet dificult problem, which is traditionally tackled by a time-consuming trial-and-error process. In this chapter, we develop an automatic fuzzy-rule-based learning method for approximating the concerned system from a set of input-output data. The approach consists of two stages: (1) Using the hybrid messy genetic algorithm (mGA) together with a new coding technique, both structure and parameters of the zero-order Takagi-Sugeno fuzzy model are coarsely optimized. The mGA is well suited to this task because of its flexible representability of fuzzy inference systems: (2) The identified fuzzy inference system is then fine-tuned by the gradient descent method. In order to demonstrate the usefulness of the proposed scheme, we finally apply the method to approximating the chaotic Mackey-Glass equation.

Original languageEnglish
Title of host publicationIntegration of Fuzzy Logic and Chaos Theory
EditorsZhong Li, Wolfgang Halang, Guanrong Chen
Pages81-97
Number of pages17
DOIs
Publication statusPublished - 2006

Publication series

NameStudies in Fuzziness and Soft Computing
Volume187
ISSN (Print)1434-9922

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computational Mathematics

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    Lee, H. J., Park, J. B., & Joo, Y. H. (2006). Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling. In Z. Li, W. Halang, & G. Chen (Eds.), Integration of Fuzzy Logic and Chaos Theory (pp. 81-97). (Studies in Fuzziness and Soft Computing; Vol. 187). https://doi.org/10.1007/11353379_4