Li YaJun, Huang Zhaowen
Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan 528300, China.
Comput Intell Neurosci. 2015;2015:389250. doi: 10.1155/2015/389250. Epub 2015 Aug 5.
The passivity problem for a class of stochastic neural networks systems (SNNs) with varying delay and leakage delay has been further studied in this paper. By constructing a more effective Lyapunov functional, employing the free-weighting matrix approach, and combining with integral inequality technic and stochastic analysis theory, the delay-dependent conditions have been proposed such that SNNs are asymptotically stable with guaranteed performance. The time-varying delay is divided into several subintervals and two adjustable parameters are introduced; more information about time delay is utilised and less conservative results have been obtained. Examples are provided to illustrate the less conservatism of the proposed method and simulations are given to show the impact of leakage delay on stability of SNNs.
本文进一步研究了一类具有变时滞和泄漏时滞的随机神经网络系统（SNNs）的无源问题。通过构造一个更有效的Lyapunov泛函，采用自由加权矩阵方法，并结合积分不等式技术和随机分析理论，提出了时滞依赖条件，使得SNNs在保证性能的情况下渐近稳定。将时变时滞划分为若干子区间并引入两个可调参数；利用了更多关于时滞的信息，得到了保守性更小的结果。通过例子说明了所提方法的保守性更小，并通过仿真展示了泄漏时滞对SNNs稳定性的影响。
