By Topic

A dynamic neural network model for nonlinear system identification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Chi-Hsu Wang ; Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan ; Pin-Cheng Chen ; Ping-Zong Lin ; Tsu-Tian Lee,

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.

Published in:

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

Date of Conference:

10-12 Aug. 2009