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 Dec 11

Publication series

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

Fingerprint

Fuzzy Identification
Hybrid Genetic Algorithm
Fuzzy Inference System
Model Identification
Chaotic systems
Fuzzy inference
Fuzzy Model
System Modeling
Chaotic System
Identification (control systems)
Genetic algorithms
Gradient Descent Method
Representability
Takagi-Sugeno Fuzzy Model
Trial and error
Fuzzy rules
Fuzzy Rules
System Identification
Complex Systems
Coding

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computational Mathematics

Cite this

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
Lee, Ho Jae ; Park, Jin Bae ; Joo, Young Hoon. / Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling. Integration of Fuzzy Logic and Chaos Theory. editor / Zhong Li ; Wolfgang Halang ; Guanrong Chen. 2006. pp. 81-97 (Studies in Fuzziness and Soft Computing).
@inbook{ebcbfea59b03443dbdc54e420b564d01,
title = "Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling",
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.",
author = "Lee, {Ho Jae} and Park, {Jin Bae} and Joo, {Young Hoon}",
year = "2006",
month = "12",
day = "11",
doi = "10.1007/11353379_4",
language = "English",
isbn = "3540268995",
series = "Studies in Fuzziness and Soft Computing",
pages = "81--97",
editor = "Zhong Li and Wolfgang Halang and Guanrong Chen",
booktitle = "Integration of Fuzzy Logic and Chaos Theory",

}

Lee, HJ, Park, JB & Joo, YH 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. Studies in Fuzziness and Soft Computing, vol. 187, pp. 81-97. https://doi.org/10.1007/11353379_4

Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling. / Lee, Ho Jae; Park, Jin Bae; Joo, Young Hoon.

Integration of Fuzzy Logic and Chaos Theory. ed. / Zhong Li; Wolfgang Halang; Guanrong Chen. 2006. p. 81-97 (Studies in Fuzziness and Soft Computing; Vol. 187).

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling

AU - Lee, Ho Jae

AU - Park, Jin Bae

AU - Joo, Young Hoon

PY - 2006/12/11

Y1 - 2006/12/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33845266216&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33845266216&partnerID=8YFLogxK

U2 - 10.1007/11353379_4

DO - 10.1007/11353379_4

M3 - Chapter

SN - 3540268995

SN - 9783540268994

T3 - Studies in Fuzziness and Soft Computing

SP - 81

EP - 97

BT - Integration of Fuzzy Logic and Chaos Theory

A2 - Li, Zhong

A2 - Halang, Wolfgang

A2 - Chen, Guanrong

ER -

Lee HJ, Park JB, Joo YH. Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling. In Li Z, Halang W, Chen G, editors, Integration of Fuzzy Logic and Chaos Theory. 2006. p. 81-97. (Studies in Fuzziness and Soft Computing). https://doi.org/10.1007/11353379_4