| Augmented Lyapunov Approach to Exponential Stability of Discrete-Time Neural Networks |
| Received:March 30, 2009 Revised:April 26, 2010 |
| Key Words:
discrete-time neural networks robust exponential stability delay-dependent criterion time-varying delay.
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| Fund Project:Supported by the Science and Technology Founation of Guizhou Province (Grant No.[2010]2139) and the Program for New Century Excellent Talents in University (Grant No.NCET-06-0811). |
| Author Name | Affiliation | | Zi Xin LIU | School of Mathematics and Statistics, Guizhou College of Finance and Economics, Guizhou 550004, P. R. China | | Shu L\"{U} | School of Mathematical Sciences, University of Electronic Science and Technology of China, Sichuan 611731, P. R. China | | Shou Ming ZHONG | School of Mathematical Sciences, University of Electronic Science and Technology of China, Sichuan 611731, P. R. China | | Mao YE | School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan 611731, P. R. China |
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| Abstract: |
| This paper addresses the problem of robust stability for a class of discrete-time neural networks with time-varying delay and parameter uncertainties. By constructing a new augmented Lyapunov-Krasovskii function, some new improved stability criteria are obtained in forms of linear matrix inequality (LMI) technique. Compared with some recent results in the literature, the conservatism of these new criteria is reduced notably. Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results. |
| Citation: |
| DOI:10.3770/j.issn:1000-341X.2011.03.013 |
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