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A Delay-Dependent Approach to Robust Stability for Uncertain Stochastic Neural Networks with Time-Varying Delay
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並列摘要
This paper investigates the global delay-dependent robust stability in the mean square for uncertain stochastic neural networks with time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on a linear matrix inequality approach, globally delay-dependent robust stability criterion is derived by introducing some relaxation matrices which, when chosen properly, lead to a less conservative result. Two numerical examples are given to illustrate the effectiveness of the method.
延伸閱讀
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- LIAO, X., YANG, X., & ZHANG, W. (2006). DELAY-DEPENDENT ASYMPTOTIC STABILITY FOR NEURAL NETWORKS WITH TIME-VARYING DELAYS. Discrete Dynamics in Nature and Society, 2006(), 815-839. https://doi.org/10.1155/DDNS/2006/91725
- Lu, C., & Wang, L. (2014). Robust Exponential Stability of Impulsive Stochastic Neural Networks with Leakage Time-Varying Delay. Abstract and Applied Analysis, 2014(), 160-167-1416. https://doi.org/10.1155/2014/831027
- Mao, W. H., Deng, F. Q., & Wan, A. H. (2012). Delay-Dependent Robust Exponential Stability for Uncertain Neutral Stochastic Systems with Interval Time-Varying Delay. Journal of Applied Mathematics, 2012(), 1080-1101-468. https://doi.org/10.1155/2012/593780