Self-Organization and Basis Functions of Neural Network Controllers

Takayuki Yamada; Norifumi Yasue; Takenori Morimitsu

doi:10.20965/jrm.1996.p0333

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JRM Vol.8 No.4 pp. 333-337

doi: 10.20965/jrm.1996.p0333

(1996)

Paper:

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Self-Organization and Basis Functions of Neural Network Controllers

Takayuki Yamada, Norifumi Yasue and Takenori Morimitsu

NTT Access Network Systems Laboratories, Shirane 162, Ooaza Shirakata, Tokaimura, Naka-gun, Ibaragi-Pref., 319-11, Japan

Received:

November 30, 1995

Accepted:

December 15, 1995

Published:

August 20, 1996

Keywords:

Self-organization, RBF, Neural network, Nonlinear, Learning

Abstract

This paper discusses a relationship between a controller for an unknown nonlinear plant and a self-organization capability which changes the structure of the controller. The controllers using both a neural network and a basis function have the potential to realize a nonlinear control system with self-organization capability because they can change their structure through the change of the number of neurons or basis functions.

Cite this article as:

T. Yamada, N. Yasue, and T. Morimitsu, “Self-Organization and Basis Functions of Neural Network Controllers,” J. Robot. Mechatron., Vol.8, No.4, pp. 333-337, 1996.

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