Delay-Distribution-Dependent Exponential Stability Criteria for Discrete-Time Recurrent Neural Networks With Stochastic Delay
Delay-Distribution-Dependent Exponential Stability Criteria for Discrete-Time Recurrent Neural Networks With Stochastic Delay
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DOI：
10.1109/tnn.2008.2000166
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发表时间：
2008-07
影响因子：
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通讯作者：
D. Yue;Yijun Zhang;E. Tian;Chen Peng
中科院分区：
文献类型：
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作者：
D. Yue;Yijun Zhang;E. Tian;Chen Peng
本简介涉及一类具有随机延迟的线性离散时间循环神经网络 (DRNN) 均方意义上的全局指数稳定性分析问题。与现有研究工作不同的是，该方法同时涉及时延变化范围和概率分布的影响。首先，提出了一种建模方法，将时延的概率分布转化为变换后的DRNN模型的参数矩阵，其中时延由随机二元分布变量来表征。基于新方法，通过使用 Lyapunov-Krasovskii 泛函并利用一些新的分析技术，研究了具有随机延迟的 DRNN 的均方意义上的全局指数稳定性。数值算例说明了该方法的有效性和适用性。
This brief is concerned with the analysis problem of global exponential stability in the mean square sense for a class of linear discrete-time recurrent neural networks (DRNNs) with stochastic delay. Different from the prior research works, the effects of both variation range and probability distribution of the time delay are involved in the proposed method. First, a modeling method is proposed by translating the probability distribution of the time delay into parameter matrices of the transformed DRNN model, where the delay is characterized by a stochastic binary distributed variable. Based on the new method, the global exponential stability in the mean square sense for the DRNNs with stochastic delay is investigated by using the Lyapunov-Krasovskii functional and exploiting some new analysis techniques. A numerical example is provided to show the effectiveness and the applicability of the proposed method.