Mean-square exponential input-to-state stability of stochastic recurrent neural networks with multi-proportional delays
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- 作者：Liqun Zhou<sup> ; sup><sup>zhouliqun20000@163.comsup>Author Vitae ; Xueting LiuAuthor Vitae
- 关键词：Stochastic neural networks ; Proportional delay ; Mean-square exponential input-to-state stability ; Lyapunov-Krasovskii functional
- 刊名：Neurocomputing
- 出版年：2017
- 出版时间：5 January 2017
- 年：2017
- 卷：219
- 期：Complete
- 页码：396-403
- 全文大小：641 K
- 卷排序：219
文摘
This paper investigates mean-square exponential input-to-state stability of stochastic recurrent neural networks with multi-proportional delays. Here, we study the proportional delay, which is a kind of unbounded time-varying delay in stochastic recurrent neural networks, by employing Lyapunov-Krasovskii functional, stochastic analysis theory and It o^′s formula. A new stability criterion about the mean-square exponential input-to-state stability, which is different from the traditional stability criteria, is presented. In addition, the new proposed criterion easy to verify and less conservation than earlier publications about mean-square exponential input-to-state stability of stochastic recurrent neural networks. Finally, several examples and their simulations are given to illustrate the correctness and effectiveness of the theoretical results.