𝔖 Bobbio Scriptorium
✦   LIBER   ✦

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

✍ Scribed by Dug Hun Hong; Changha Hwang; Chulhwan Ahn

Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
244 KB
Volume
142
Category
Article
ISSN
0165-0114

No coin nor oath required. For personal study only.

✦ Synopsis

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.

πŸ“œ SIMILAR VOLUMES

Fuzzy regression model with fuzzy input
Fuzzy regression model with fuzzy input and output data for manpower forecasting
✍ Hong Tau Lee; Sheu Hua Chen πŸ“‚ Article πŸ“… 2001 πŸ› Elsevier Science 🌐 English βš– 113 KB

In modeling a fuzzy system with fuzzy linear functions, the vagueness of the fuzzy output data may be caused by both the indeΓΏniteness of model parameters and the vagueness of the input data. This situation occurs as the input data are envisaged as facts or events of an observation which are uncontr