具有马尔可夫跳跃和时变混合时滞的离散随机神经网络的渐近稳定性判据

Hongjun Chu, Fang Wang, Lixin Gao
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引用次数: 0

摘要

研究了一类具有马尔可夫跳变参数和时变混合时滞的离散随机递归神经网络的全局渐近稳定性问题。混合时滞包括离散时滞和分布时滞,并假设离散时滞和分布时滞都是时变的,属于一个给定的区间,这意味着区间时变时滞的下界和上界是可用的。神经网络具有有限数量的模态,并且模态可以根据离散马尔可夫链从一个模态跳到另一个模态。基于Lyapunov方法和随机分析方法,得到了线性矩阵不等式(LMI)的时滞区间相关稳定性判据,并对已有结果进行了推广。最后,通过数值算例验证了所提结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An asymptotical stability criterion for discrete-time stochastic neural networks with Markovian jumping and time-varying mixed delays
The global asymptotical stability problem is considered for a class of discrete-time stochastic recurrent neural networks(NNs) with Markovian jumping parameters and time-varying mixed delays in this paper. The mixed time delays include discrete delays and distributed delays, and both are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. The neural networks have a finite number of modes, and the modes may jump from one to another according to a discrete-time Markov chain. Based on the Lyapunov method and stochastic analysis approach, delay-interval dependent stability criterion is obtained in terms of linear matrix inequality(LMI) and generalizes existing results. Finally, a numerical example is given to demonstrate the effectiveness of the proposed results.
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