Article

Identification of fuzzy parameters by interval regression models

  1. Hisao Ishibuchi Member†,
  2. Hideo Tanaka Nonmember‡

Article first published online: 11 SEP 2007

DOI: 10.1002/ecjc.4430731203

Issue

Electronics and Communications in Japan (Part III: Fundamental Electronic Science)

Electronics and Communications in Japan (Part III: Fundamental Electronic Science)

Volume 73, Issue 12, pages 19–27, 1990

Additional Information

How to Cite

Ishibuchi, H. and Tanaka, H. (1990), Identification of fuzzy parameters by interval regression models. Electron. Comm. Jpn. Pt. III, 73: 19–27. doi: 10.1002/ecjc.4430731203

Author Information

  1. Nonmember Faculty of Engineering, University of Osaka Prefecture, Sakai, Japan 591

  1. †

    Hasao Ishibuchi graduared in 1985 from Dept. Prec. Mech. Eng., Fac. Eng., Kyoto Univ., where he obtained a Master's degree in 1987. He than became an Assistant in thedept. ind. Eng., Fac. Eng. Univ. of Osaka Pref. He is engaged in research on interval models and fuzzy models. He is a member of system contr.; inf. Soc., Jap.; OR Soc.; and Jap. Ind. Manag. Assoc.

  2. ‡

    Hideo Tanaka graduated in 1962 from Dept. Instr., Fac. Eng., Kobe Univ., and affiliated with Daikin Co. He obtained a Dr. of Eng. degree in 1969 from Osaka City Univ. He was an Assistant in 1969, then served as Lecturer, Assoc. Prof., and in 1987 Prof. in the Dept. Ind. Eng., Fac. Eng., Univ. of Osaka Pref. tems. He is a member of IFSA; SOC. Instr. Contr. Eng.; Jap. OR SOC.; Jap. Ind. Manag. Assoc.; and IEEE. He is engaged in research on the theory of fuzzy systems. He is a member of IFSA; Soc. Instr. Contr. Eng.; Jap. OR Soc.; Jap. Ind. Manag. Assoc.; and IEEE.

Publication History

  1. Issue published online: 11 SEP 2007
  2. Article first published online: 11 SEP 2007

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Abstract

Tanaka et al. formulated the problem of determining the linear regression expression with fuzzy parameters as a fuzzy regression analysis problem, using the data for the input-output relation. This paper discusses such a fuzzy regression analysis, and proposes a method which identifies the fuzzy parameters using the interval regression expression.

First, a method is proposed which identifies the fuzzy parameters with symmetrical triangular form from the interval regression expression. Then a method is proposed which identifies the fuzzy parameters with asymmetrical trapezoid form from two interval regression expressions. Using numerical examples, a method is illustrated which identifies the asymmetrical trapezoid fuzzy parameters from the usual point data and the fuzzy data.

The method proposed herein employs the interval regression expression based on the interval operations, which leads to the merit that the procedure is simple and easy to understand compared with the conventional method employing the operations of fuzzy numbers. The fuzzy regression expression with asymmetrical trapezoid fuzzy parameters is a generalization of the fuzzy regression expression with symmetrical fuzzy parameters obtained by the conventional method.

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