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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Passivity of Memristive BAM Neural Networks with Probabilistic and Mixed Time-Varying Delays
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Passivity of Memristive BAM Neural Networks with Probabilistic and Mixed Time-Varying Delays

机译:缺点BAM神经网络与概率和混合时变延迟的钝化性

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摘要

This paper is concerned with the passivity problem of memristive bidirectional associative memory neural networks (MBAMNNs) with probabilistic and mixed time-varying delays. By applying random variables with Bernoulli distribution, the information of probability time-varying delays is taken into account. Furthermore, we consider the probability distribution of the variation and the extent of the delays; therefore, the results derived are less conservative than in the existing papers. In particular, the leakage delays as well as distributed delays are all taken into consideration. Based on appropriate Lyapunov-Krasovskii functionals (LKFs) and some useful inequalities, several conditions for passive performance are established in linear matrix inequalities (LMIs). Finally, numerical examples are given to demonstrate the feasibility of the presented theories, and the results reveal that the probabilistic and mixed time-varying delays have an unstable influence on the system and should not be ignored.
机译:本文涉及具有概率和混合时变延迟的Memristive双向关联记忆神经网络(MBAMNNS)的被动问题。通过将随机变量应用于Bernoulli分布,考虑了概率时变延迟的信息。此外,我们考虑变异的概率分布和延迟程度;因此,衍生的结果较少保守而不是现有论文。特别地,泄漏延迟以及分布式延迟都得到考虑。基于适当的Lyapunov-Krasovskii功能(LKFS)和一些有用的不等式,在线性矩阵不等式(LMI)中建立了几种被动性能的条件。最后,给出了数值示例来证明所提出的理论的可行性,结果表明,概率和混合的时变延迟对系统产生了不稳定的影响,不应被忽略。

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