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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Global Exponential Stability of Memristive Neural Networks With Mixed Time-Varying Delays
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Global Exponential Stability of Memristive Neural Networks With Mixed Time-Varying Delays

机译：混合时变延迟的忆阻神经网络的全局指数稳定性

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摘要

This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays (DMNNs). By means of inequality techniques, theories of the M-matrix, and the comparison strategy, the Lagrange exponential stability of the underlying DMNNs is considered in the sense of Filippov, and the globally exponentially attractive set is estimated through employing the M-matrix and external input. Especially, when the external input is not concerned, the Lyapunov exponential stability of the corresponding DMNNs is developed immediately in the form of an M-matrix, which contains some published outcomes as special cases. Furthermore, by constructing an M-matrix-based differential system, the Lyapunov exponential stability of the DMNNs is studied, which is less conservative than some existing ones. Finally, three simulation examples are carried out to examine the validness of the theories.
机译：本文通过离散和分布时变延迟（DMNN）来调查忆内神经网络的拉格朗日指数稳定性和Lyapunov指数稳定性。 通过不等式技术，M-矩阵的理论和比较策略，在Filippov的意义上考虑了基础DMNN的拉格朗日指数稳定性，并且通过采用M-Matrix和外部来估计全球指数上具有吸引力的集合 输入。 特别是，当外部输入未涉及时，相应DMNN的Lyapunov指数稳定性立即以M-Matrix的形式开发，其包含一些已公布的结果作为特殊情况。 此外，通过构建基于M矩阵的差分系统，研究了DMNN的Lyapunov指数稳定性，其比某些现有的差别较少。 最后，进行了三个模拟示例以检查理论的有效性。

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