By Topic

Global exponential convergence of Cohen-Grossberg neural networks with time delays

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

3 Author(s)
Hongtao Lu ; Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China ; Ruiming Shen ; Fu-lai Chung

In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.

Published in:

Neural Networks, IEEE Transactions on  (Volume:16 ,  Issue: 6 )