Adaptive nonlinear system modeling using independent component analysis and neuro-fuzzy method

Author

Kim, Sung-Soo ; Kwak, Keun-Chang ; Ryu, Jeong-Woong ; Oh, Bum-Jin ; Hong, Jun-Sik

Author_Institution

Dept. of Electr. Eng., Woosuk Univ., Chonbuk, South Korea

Volume

2

fYear

2000

fDate

Oct. 29 2000-Nov. 1 2000

Firstpage

828

Abstract

This paper represents a new approach to modeling a nonlinear system using the independent component analysis (ICA) and adaptive neuro-fuzzy inference system (ANFIS). To improve the performance of the model system, a set of inputs is transformed to be statistically independent using ICA as a preprocessing to the ANFIS established based on fuzzy c-means (FCM). The performance of the proposed method is demonstrated by applying it to the Box and Jenkins furnace data. The results of the computer simulation are demonstrated for the validity of this algorithm.

Keywords

adaptive systems; furnaces; fuzzy neural nets; nonlinear systems; statistical analysis; ICA; adaptive neuro-fuzzy inference system; adaptive nonlinear system modeling; algorithm; computer simulation results; fuzzy c-means; gas furnace data; independent component analysis; model system performance; neuro-fuzzy method; statistically independent inputs; Adaptive systems; Data preprocessing; Furnaces; Fuzzy control; Fuzzy sets; Fuzzy systems; Independent component analysis; Nonlinear systems; Principal component analysis; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2000. Conference Record of the Thirty-Fourth Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-6514-3

Type

conf

DOI

10.1109/ACSSC.2000.910629

Filename

910629