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Existence and exponential stability of periodic solution for BAM neural networks with periodic coefficients and delays

机译:具有周期系数和时滞的BAM神经网络周期解的存在性和指数稳定性

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摘要

In this paper, the existence and global exponential stability of the periodic solution are discussed for the bidirectional associative memory (BAM) neural networks with periodic coefficients and delays. Some new sufficient conditions for ascertaining the existence and global exponential stability of the periodic solution of such BAM neural networks are obtained by using the properties of nonsingular M-matrix, integral inequality analysis and a continuation theorem based on coincidence degree. These conclusions are presented not only in terms of systems parameters but also the period of the system and the mean values of decaying rates. Therefore, the results are fairly new. Moreover, some results from previous works are extended and improved. These results are helpful to design globally exponentially stable BAM networks and periodic oscillatory neural networks.
机译:本文讨论了具有周期系数和时滞的双向联想记忆(BAM)神经网络周期解的存在性和全局指数稳定性。利用非奇异M矩阵的性质,积分不等式分析和基于重合度的连续定理,为确定此类BAM神经网络的周期解的存在性和全局指数稳定性提供了一些新的充分条件。这些结论不仅以系统参数的形式给出,而且以系统的周期和衰减率的平均值表示。因此,结果是相当新的。此外,先前工作的一些结果得到扩展和改进。这些结果有助于设计全局指数稳定的BAM网络和周期振荡神经网络。

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