A transformed input-domain approach to fuzzy modeling

Author

Kim, Euntai ; Park, Minkee ; Kim, Seungwoo ; Park, Mignon

Author_Institution

Dept. of Electron. Eng., Yonsei Univ., Seoul, South Korea

Volume

6

Issue

4

fYear

1998

fDate

11/1/1998 12:00:00 AM

Firstpage

596

Lastpage

604

Abstract

This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm

Keywords

correlation theory; fuzzy systems; modelling; PCA; fuzzy modeling; input space partitioning; principal component analysis; sample data component correlation; transformed input-domain approach; Computer simulation; Covariance matrix; Fuzzy systems; Helium; Humans; Input variables; Karhunen-Loeve transforms; Microwave integrated circuits; Neural networks; Partitioning algorithms;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.728458

Filename

728458