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New Results on Passivity Analysis of Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

机译:时变时滞和泄漏时滞的随机神经网络无源性分析的新结果

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摘要

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.
机译:进一步研究了一类具有变化的时滞和泄漏时滞的随机神经网络系统的无源性问题。通过构造更有效的Lyapunov泛函,采用自由加权矩阵法,并结合积分不等式技术和随机分析理论,提出了时滞相关条件,使得SNN渐近稳定并保证了性能。时变延迟分为几个子间隔,并引入了两个可调参数。利用了更多有关时间延迟的信息,而获得的保守性却较低。通过实例说明了该方法的保守性,并通过仿真表明了泄漏延迟对SNNs稳定性的影响。

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