A dynamic neural network model for nonlinear system identification

Author

Wang, Chi-Hsu ; Chen, Pin-Cheng ; Lin, Ping-Zong ; Lee, Tsu-Tian

Author_Institution

Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan

fYear

2009

fDate

10-12 Aug. 2009

Firstpage

440

Lastpage

441

Abstract

In this paper, a new dynamic neural network based on the Hopfield neural network is proposed to perform the nonlinear system identification. Convergent analysis is performed by the Lyapunov-like criterion to guarantee the error convergence during identification. Simulation results demonstrate that the proposed dynamic neural network trained by the Lyapunov approach can obtain good identified performance.

Keywords

Hopfield neural nets; Lyapunov methods; convergence; identification; nonlinear systems; Hopfield neural network; Lyapunov-like criterion; adaptive training law; convergent analysis; dynamic neural network model; nonlinear system identification; Control systems; Convergence; Electronic mail; Force feedback; Hopfield neural networks; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; System identification; Hopfield neural network; Lyapunov criterion; dynamic neural network; system identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Reuse & Integration, 2009. IRI '09. IEEE International Conference on

Conference_Location

Las Vegas, NV

Print_ISBN

978-1-4244-4114-3

Electronic_ISBN

978-1-4244-4116-7

Type

conf

DOI

10.1109/IRI.2009.5211647

Filename

5211647