Journal
NEUROCOMPUTING
Volume 73, Issue 4-6, Pages 1038-1047Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2009.10.001
Keywords
Delay-dependent; Asymptotic stability; Neural networks (NNs); Linear matrix inequality (LMI)
Categories
Funding
- National Science Foundation of China [60904025, 60904026]
- Postdoctoral Foundation of Jiangsu Province [0901026c]
- Key Laboratory of Education Ministry for Image Processing and Intelligent Control [200805]
Ask authors/readers for more resources
In this paper, the problem on global asymptotic stability analysis for a class of neural networks (NNs) with time-varying delays and general activation functions is considered. By employing a novel augmented Lyapunov-Krasoviskii functional (LKF), an improved stability condition is obtained in linear matrix inequalities form. The special cases of the obtained criterion turn out to be equivalent to some existing results but include the less number of variables. With the present stability conditions, the computational burden and conservatism are largely reduced. Examples are provided to demonstrate the advantage of the stability results. (C) 2009 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available