Nonlinear System Identification Based on Genetic Algorithm and Grey Function

Author

Wang, Zhelong ; Gu, Hong

Author_Institution

Dalian Univ. of Technol., Dalian

fYear

2007

fDate

18-21 Aug. 2007

Firstpage

1741

Lastpage

1744

Abstract

The paper presents a method for the identification of nonlinear system parameters by using an improved Genetic Algorithm and Grey Function. The paper firstly outlines several commonly used nonlinear identification methods such as RLS, RIV and COR and also their drawbacks. Then, a method based on the Genetic Algorithm and Grey Function is proposed and given in detail in the paper. Finally, a simulation experiment to TV set production data of an electronic factory was carried out. The simulations show that the method can gain good results and is also simple and effective.

Keywords

genetic algorithms; grey systems; nonlinear systems; recursive estimation; correlative function method; genetic algorithm; grey function; nonlinear system identification; recursive instrumental variable method; recursive least squares method; Biological system modeling; Environmental economics; Equations; Genetic algorithms; Investments; Nonlinear systems; Predictive models; Production; Resonance light scattering; Uncertain systems; Genetic Algorithm; Grey function; Nonlinear system;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2007 IEEE International Conference on

Conference_Location

Jinan

Print_ISBN

978-1-4244-1531-1

Type

conf

DOI

10.1109/ICAL.2007.4338854

Filename

4338854