Approximation of a neural network controller based on model reference technique to identify a non-linear system

Author

Mandal, P. ; Deb, Abhishek

Author_Institution

Dept. of Appl. Phys., Univ. of Calcutta, Kolkata, India

fYear

2014

fDate

Jan. 31 2014-Feb. 2 2014

Firstpage

21

Lastpage

25

Abstract

In this paper, a neural network (NN) controller is approximated by method of `training´, using a reference model, to identify a plant. At first, the plant identification neural network, is realized by learning the behaviour of a given nonlinear plant. The outputs of this neural network and the actual plant are compared. The knowledge of the comparison is fed back to the NN controller as its input. The NN controller is realized in such a way that it will control the plant behaviour as a reference.

Keywords

neurocontrollers; nonlinear systems; NN controller; behaviour learning; model reference technique; neural network controller approximation; nonlinear plant; nonlinear system; plant identification neural network; reference model; training; Approximation methods; Artificial neural networks; Data models; Instruments; Mathematical model; Training; neural network; neural network reference model controller; neural network training; plant identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on

Conference_Location

Calcutta

Type

conf

DOI

10.1109/CIEC.2014.6959042

Filename

6959042