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
β¦ LIBER β¦
Input-output mathematical model with T-fuzzy data
β Scribed by Bing-yuan Cao
- Publisher
- Elsevier Science
- Year
- 1993
- Tongue
- English
- Weight
- 476 KB
- Volume
- 59
- Category
- Article
- ISSN
- 0165-0114
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Fuzzy regression model with fuzzy input
β
Hong Tau Lee; Sheu Hua Chen
π
Article
π
2001
π
Elsevier Science
π
English
β 113 KB
A note on fuzzy regression model with fu
β
Dug Hun Hong; Hwa-Cho Yi
π
Article
π
2003
π
Elsevier Science
π
English
β 229 KB
Multiobjective fuzzy linear regression a
β
Masatoshi Sakawa; Hitoshi Yano
π
Article
π
1992
π
Elsevier Science
π
English
β 534 KB
Ridge estimation for regression models w
β
Dug Hun Hong; Changha Hwang; Chulhwan Ahn
π
Article
π
2004
π
Elsevier Science
π
English
β 244 KB
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 (
Piecewise regression for fuzzy inputβout
β
Jing-Rung Yu; Chien-Wei Lee
π
Article
π
2010
π
Elsevier Science
π
English
β 397 KB
Studies on the output of fuzzy controlle
β
Ching-Chang Wong; Chih-Hsun Chou; Don-Lin Mon
π
Article
π
1993
π
Elsevier Science
π
English
β 473 KB