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| 离散神经网络指数稳定的增强李亚普诺夫方法 | |
| 引用本文： | 刘自鑫,吕恕,钟守铭,叶茂.离散神经网络指数稳定的增强李亚普诺夫方法[J].数学研究及应用,2011,31(3):479-489. |
| 作者姓名： | 刘自鑫 吕恕 钟守铭 叶茂 |
| 作者单位： | 贵州财经学院数学与统计学院, 贵州 贵阳 550004;电子科技大学数学科学学院, 四川 成都 611731;电子科技大学数学科学学院, 四川 成都 611731;电子科技大学计算机科学与工程学院, 四川 成都 611731 |
| 基金项目： | 贵州省科学技术基金(Grant No.[2010]2139); 教育部新世纪优秀人才计划(Grant No.NCET-06-0811). |
| 摘 要： | 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. |
| 关 键 词： | discrete-time neural networks robust exponential stability delay-dependent criterion time-varying delay. |
| 收稿时间： | 2009/3/30 0:00:00 |
| 修稿时间： | 2010/4/26 0:00:00 |
Augmented Lyapunov Approach to Exponential Stability of Discrete-Time Neural Networks |
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| Zi Xin LIU,Shu L\"{U},Shou Ming ZHONG and Mao YE.Augmented Lyapunov Approach to Exponential Stability of Discrete-Time Neural Networks[J].Journal of Mathematical Research with Applications,2011,31(3):479-489. | |
| Authors: | Zi Xin LIU Shu L\"{U} Shou Ming ZHONG and Mao YE |
| Institution: | 1. School of Mathematics and Statistics, Guizhou College of Finance and Economics,Guizhou 550004, P. R. China 2. School of Mathematical Sciences, University of Electronic Science and Technology of China,Sichuan 611731, P. R. China 3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan 611731, P. R. China |
| 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. |
| Keywords: | discrete-time neural networks robust exponential stability delay-dependent criterion time-varying delay |
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