A New Delay-Dependent Stability Condition for Stochastic Neural Networks of Neutral-Type with Multiple Discrete and Unbounded Distributed Delays

作者：Liu Guo Quan^*; Yang Simon X; Chai Yi; Fu Wei

来源：Applied Mathematics and Information Sciences, 2013, 7(2): 475-480.

DOI：10.12785/amis/072L14

• Delay-dependent stability
• stochastic neural networks
• linear matrix inequality
• neutral-type
• unbounded distributed delays
• multiple discrete delays

This paper deals with the delay-dependent asymptotic stability analysis problem for stochastic neural networks of neutraltype with mixed delays. The mixed delays comprise both multiple discrete and unbounded distributed delays. To the best of the authors' knowledge, till now, the asymptotic stability problem for this class of neural networks has not yet been solved since neutral-type delays are considered in this paper. The main objective of this paper is to fill this gap. By using Lyapunov-Krasovskii functional method and the linear matrix inequality (LMI) technique, a novel sufficient condition is derived to guarantee the global asymptotic stability of the equilibrium point in the mean square. In particular, the proposed stability condition is presented in terms of LMI, which can be easily solved by some standard numerical packages. In addition, an example is given to show the effectiveness of the obtained result.

• 出版日期2013-6
• 单位东华理工大学; 重庆大学