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JACIII Vol.11 No.5 pp. 522-532
doi: 10.20965/jaciii.2007.p0522
(2007)

Paper:

Interpretable Fuzzy Rules Acquisition of Coupled System Using Interactive Genetic Algorithms

Dun-Yong Lu* and Takehisa Onisawa**

*School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, P. R. China

**Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

Received:
April 10, 2007
Accepted:
May 15, 2007
Published:
June 20, 2007
Keywords:
fuzzy rules acquisition, rules interpretability, interactive genetic algorithms (IGAs), qualitative evaluation, coupled system
Abstract

This paper describes the use of the interactive genetic algorithms to acquire fuzzy control rules with good interpretability for a complex system having dependent variables and non-linear property. A chromosome is coded with integer to represent a fuzzy rule, and individuals composed of various numbers of chromosomes are evolved by GA operations. The acquired fuzzy rules are explained with the linguistic expressions for fuzzy sets. These linguistic expressions are determined through comparing with the standard fuzzy sets of linguistic variables designated in advance. To reduce human fatigue in the individual evaluation process, only quantitative evaluation with fitness functions is given at earlier stage. When a so-called better individual appears, not only quantitative evaluation but qualitative one is used to evaluate both the interpretability and control performance of the acquired fuzzy rules. The presented approach is applied to the control of the coupled system having two control objectives with multi-input/output variables. Simulation experiments show that the approach is feasible to acquire the satisfactory fuzzy rules with good interpretability and good control performance.

Cite this article as:
Dun-Yong Lu and Takehisa Onisawa, “Interpretable Fuzzy Rules Acquisition of Coupled System Using Interactive Genetic Algorithms,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.5, pp. 522-532, 2007.
Data files:
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Last updated on May. 11, 2021