状态依赖交换延迟BAM神经网络的有限时间同步

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI:10.1109/icaci55529.2022.9837484

Jinrong Yang, Guici Chen

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引用次数: 1

摘要

本文主要研究具有状态依赖交换的延迟双向联想记忆神经网络的有限时间同步问题。首先，用区间矩阵法代替微分包含理论和集值映射来解释bamnn的状态相关切换参数;然后，根据bamnn的双层结构，设计了两个相似的状态反馈控制器。通过构造Lyapunov函数，应用FNTS的定义和一些不等式技巧，得到了保证驱动响应bamnn在有限时间内达到同步的几个充分条件。此外，通过简单计算得到了FNTS的沉降时间(ST)。最后，通过数值仿真验证了本文的正确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Finite-time Synchronization of Delayed BAM Neural Networks with State-dependent Switching

This paper mainly considers the finite-time synchronization (FNTS) of delayed bidirectional associative memory neural networks (BAMNNs) with state-dependent switching. First, the state-dependent switching parameters of BAMNNs are explained via the interval matrix method instead of differential inclusion theory and set-value map. Then, two similar state feedback controllers are designed due to the bilayer structure of the BAMNNs. By constructing Lyapunov function and applying the definition of FNTS and some inequality tricks, several sufficient conditions are obtained to ensure that the drive-response BAMNNs reach synchronization in finite time. In addition, the settling time (ST) of FNTS is obtained by simple calculations. Finally, the correctness of this paper is verified by numerical simulations.

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2022 14th International Conference on Advanced Computational Intelligence (ICACI)

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