Tracking control using self-organizing neural network

Author

Yamashita, Yuh ; Ikuno, Yayo ; Shima, Masasuke

Author_Institution

Grad. School of Inf. Sci., Nara Inst. of Sci. & Technol., Japan

Volume

4

fYear

1996

fDate

11-13 Dec 1996

Firstpage

3804

Abstract

An identification method and a tracking controller for nonlinear discrete-time systems using the “neural-gas network” are proposed. The neural-gas network is a kind of self-organizing network, and was developed by Martinet and Schulten (1991). The system is identified by estimating a hypersurface in the space of input and output sequences using the neural-gas network. The metric of the space of the synapse weight is modified to increase efficiency of learning. The hypersurface is expressed with a method by means of rational Bezier surface or direct interpolation. An inverse model of the system is derived from the surface, which is applied to a tracking control problem

Keywords

discrete time systems; identification; interpolation; neurocontrollers; nonlinear control systems; self-organising feature maps; tracking; unsupervised learning; direct interpolation; hypersurface; identification method; inverse model; neural-gas network; nonlinear discrete-time systems; rational Bezier surface; self-organizing neural network; synapse weight; tracking control; Control systems; Difference equations; Information science; Interpolation; Network topology; Neural networks; Nonlinear control systems; Organizing; Systems engineering and theory; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.577243

Filename

577243