A design of generalized minimum variance controllers using a GMDH-type neural network for nonlinear systems

Author

Sakaguchi, A. ; Yamamoto, T. ; Kaneda, M.

Author_Institution

Dept. of Commun. Eng., Okayama Prefectural Univ., Japan

Volume

4

fYear

1999

fDate

1999

Firstpage

1090

Abstract

Describes the design of a generalized minimun variance controller (GMVC) using a GMDH-type neural network for nonlinear systems. The predictive value of the output required in the GMVC law is obtained by using a group method of data handling (GMDH) which is a kind of multilayered neural network. Since the predictive value of the output in GMVC law is calculated by a nonlinear model, one can expect a better control performance than that calculated by a linear model. The behavior of the proposed control scheme is evaluated on a numerical simulation example

Keywords

control system synthesis; forecasting theory; identification; multilayer perceptrons; neurocontrollers; nonlinear control systems; GMDH-type neural network; generalized minimum variance controllers; group method of data handling; multilayered neural network; Artificial neural networks; Communication system control; Control systems; Design engineering; Mathematical model; Neural networks; Nonlinear control systems; Nonlinear systems; Numerical simulation; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.812562

Filename

812562