By Topic

MATLAB Simulink Modeling of Zhang Neural Network Solving for Time-Varying Pseudoinverse in Comparison with Gradient Neural Network

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)
Yunong Zhang ; Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China ; Ning Tan ; Binghuang Cai ; Zenghai Chen

A special kind of recurrent neural networks (RNN), i.e., Zhang neural networks (ZNN), has recently been proposed for online time-varying problems solving. In this paper, we generalize and investigate the Matlab Simulink modeling and verification of a ZNN model for online time-varying matrix pseudoinverse solving. Based on click-and-drag mouse operations, Simulink could be easily and conveniently used to model and simulate complicated neural systems in comparison with Matlab coding. For comparative purposes, the conventional gradient-based neural network (or termed gradient neural network, GNN) is also developed for the time-varying pseudoinverse solving. Matlab Simulink modeling results substantiate the feasibility and efficacy of ZNN on time-varying pseudoinverse solving.

Published in:

Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on  (Volume:1 )

Date of Conference:

20-22 Dec. 2008