Application of genetic algorithms to system identification

Author

Zibo, Zhang ; Naghdy, Fazel

Author_Institution

Dept. of Electr. & Comput. Eng., Wollongong Univ., NSW, Australia

Volume

2

fYear

1995

fDate

29 Nov-1 Dec 1995

Firstpage

777

Abstract

System identification is a pre-requisite to the analysis of a dynamic system and design of an appropriate controller for improving its performance. In conventional identification methods, a model structure is selected and the parameters of that model are calculated by optimising an objective function. This process usually requires a large set of input/output data from the system which is not always available. In addition the obtained parameters may be only locally optimal. In this work genetic algorithms are applied to system identification. A system is assumed to have an ARMAX model, the parameters of which are obtained using the search process of the genetic algorithms. The method developed is presented and results of its application to a number of experimental systems are described. The results obtained are quite encouraging

Keywords

autoregressive moving average processes; genetic algorithms; identification; search problems; ARMAX model; autoregressive moving average model; controller design; dynamic system; genetic algorithms; input output data; locally optimal; model structure; objective function; optimisation; parameter estimation; performance; search process; system identification; Application software; Control systems; Delay effects; Delay estimation; Genetic algorithms; MIMO; Optimization methods; Performance analysis; Poles and zeros; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1995., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2759-4

Type

conf

DOI

10.1109/ICEC.1995.487484

Filename

487484