Fuzzy regression analysis by a fuzzy neural network and its application to dual response optimization

Author

Cheng, Chi-Bin

Author_Institution

Dept. of Ind. Eng. & Manage., Chao-Yang Univ. of Technol., Taichung, Taiwan

fYear

2001

fDate

25-28 July 2001

Firstpage

2681

Abstract

Fuzzy regression analysis achieved by a fuzzy radial basis function neural network is discussed in this paper. A fuzzy regression model constructed in such a manner is then applied to a dual-response optimization problem. Fuzzy regression models are ideally suited for dual-response optimization with two advantages: (1) many systems encountered in practice are fuzzy, and (2) fuzzy regression models have dual responses in nature. The dual response optimization problem is formulated as a multiple-objective decision-making program, and an algorithm based on the duality theory is developed to solve this problem. A numerical example is also provided for illustration

Keywords

decision theory; duality (mathematics); fuzzy neural nets; fuzzy systems; mathematical programming; mathematics computing; operations research; radial basis function networks; statistical analysis; dual response optimization; duality theory; fuzzy radial basis function neural network; fuzzy regression analysis; fuzzy systems; multiple-objective decision-making programming; Chaos; Design optimization; Engineering management; Fuzzy neural networks; Fuzzy systems; Humans; Industrial engineering; Radial basis function networks; Regression analysis; Technology management;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.943647

Filename

943647