Application of genetic algorithm and recurrent network to nonlinear system identification

Author

Juang, Jih-Gau

Author_Institution

Dept. of Guidance & Commun. Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan

Volume

1

fYear

2003

fDate

23-25 June 2003

Firstpage

129

Abstract

Nonlinear system identification using recurrent neural network with genetic algorithm is presented. A continuous-time model of Hopfield neural network is used in this study. Its convergence properties are first evaluated. Then the model is implemented to identify nonlinear systems. Recurrent network´s operational factors of the system identification scheme are obtained by genetic algorithm. Mathematical formulations are introduced throughout the paper. After test, the proposed scheme can successfully identify nonlinear system within acceptable tolerance.

Keywords

Hopfield neural nets; continuous time systems; convergence; genetic algorithms; identification; nonlinear control systems; recurrent neural nets; Hopfield neural network; continuous time systems; convergence properties; genetic algorithm; mathematical formulations; nonlinear system identification; operational factors; recurrent neural network; Artificial neural networks; Biological neural networks; Genetic algorithms; Multi-layer neural network; Neural networks; Neurofeedback; Neurons; Nonlinear systems; Recurrent neural networks; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on

Print_ISBN

0-7803-7729-X

Type

conf

DOI

10.1109/CCA.2003.1223277

Filename

1223277