Paper:
Language: English:

LimNet-Flexible Learning Network Containing Linear Properties

Jinglu Hu^, Kotaro Hirasawa^ and Kousuke Kumamaru^**

*Department of Electrical and Electronic Systems Engineering, Kyushu University 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan **Department of Control Engineering and Science, Kyushu Institute of Technology 680-4 Kawatsu, lizuka 812, Japan

Received: September 25, 1998

Accepted: March 15, 1999

Keywords: Learning network, Linear model, Prior knowledge, Modeling and identification, Nonlinear system

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.3, No.4 pp. 303-311, 1999

Abstract

We propose a flexible learning network of a class of linear models. A nonlinear black box system is transformed into a network of known and unknown nodes (node functions), where a linear model is introduced. Unknown nodes are parameterized using neurofuzzy models. The resulting learning network is interpreted as a linear model network (LimNet) and features useful linear properties and universal approximation ability.

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