Passivity analysis of memristive neural networks with probabilistic time-varying delays
Passivity analysis of memristive neural networks with probabilistic time-varying delays
复制标题
具有概率时变延迟的忆阻神经网络的无源性分析
DOI：
10.1016/j.neucom.2016.01.035
复制
发表时间：
2016-05-26
期刊：
影响因子：
6
通讯作者：
Tu, Zhengwen
中科院分区：
文献类型：
--
作者：
Li, Ruoxia;Cao, Jinde;Tu, Zhengwen
本文进一步研究了一类具有概率时变时滞的记忆神经网络的无源性问题。基于有效的Lyapunov泛函和Wirtinger型不等式，给出了保证记忆模型被动性能的充分条件。通过建立具有伯努利分布的随机变量，考虑了概率时变时滞信息，并将其转化为具有确定性时变时滞和随机参数的时延信息。此外，还考虑了时延的范围及其变化的概率分布，从而使本文的结果更加合理。最后，通过两个数值算例说明了所提方法的优越性。(C)2016爱思唯尔B.V.保留所有权利。
The passivity problem has been further researched for a class of memristive neural networks with probabilistic time-varying delays in this paper. Based on an effective Lyapunov functional and the Wirtinger-type inequality, sufficient conditions are presented to guarantee the passive performance of the memristive models. By establishing a stochastic variable with Bernoulli distribution, the information of probabilistic time-varying delays are considered, which were transformed into one with deterministic time-varying delay and stochastic parameters. Moreover, the range of the delays as well as the probability distribution of its variation are all taken into consideration, thus, the results derived in this paper are more reasonable. Finally, the advantages of the proposed techniques are demonstrated by two numerical examples. (C) 2016 Elsevier B.V. All rights reserved.