Ridge estimation for regression models with crisp inputs and Gaussian fuzzy output

This paper deals with ridge estimation of fuzzy multiple linear and nonlinear regression models with crisp inputs and Gaussian fuzzy output. Using ridge regression learning algorithm in the Lagrangian dual space, we describe a ridge estimation of fuzzy multiple linear regression model of Xu and Li (...

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Bibliographic Details
Published in:Fuzzy sets and systems 2004-03, Vol.142 (2), p.307-319
Main Authors: Hong, Dug Hun, Hwang, Changha, Ahn, Chulhwan
Format: Article
Language:English
Subjects:
Applied sciences
Circuit properties
Computer science
control theory
systems
Digital circuits
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Fuzzy inference systems
Fuzzy regression
Fuzzy system models
Information, signal and communications theory
Mathematical methods
Miscellaneous
Ridge estimation
Telecommunications and information theory
Theoretical computing
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Summary:This paper deals with ridge estimation of fuzzy multiple linear and nonlinear regression models with crisp inputs and Gaussian fuzzy output. Using ridge regression learning algorithm in the Lagrangian dual space, we describe a ridge estimation of fuzzy multiple linear regression model of Xu and Li (Fuzzy Sets and Systems 119 (2001) 215). It allows us to perform nonlinear regression for Xu and Li's model by constructing a fuzzy linear regression function in a high dimensional feature space. Experimental results are then presented which indicate the performance of this algorithm.
ISSN:0165-0114
1872-6801
DOI:10.1016/S0165-0114(03)00002-2