Title :
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
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;
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
DOI :
10.1109/IRI.2009.5211647
