分数阶时滞的BAM神经网络的有限时间稳定性
Finite-Time Stability of Fractional-Order BAM Neural Networks with Time Delays
DOI: 10.12677/AAM.2023.126270, PDF,    科研立项经费支持
作者: 罗雪梅*, 李进东：成都理工大学数理学院，数学地质四川省重点实验室，四川 成都
关键词: 分数阶双向联想记忆神经网络；有限时间稳定性；时滞；Gronwall不等式；Fractional Order Bidirectional Associative Memory Neural Networks； Finite-Time Stability； Time Delays； Gronwall Inequality
摘要: 本文主要研究分数阶时滞双向联想记忆神经网络的有限时间稳定性。基于具有时滞的分数阶Gronwall不等式，得到了一个保证分数阶时滞双向联想记忆神经网络有限时间稳定的充分条件，降低了已有准则的保守性，并通过两个数值例子验证了主要结果的有效性和可行性。
Abstract: The finite-time stability of time-delayed fractional order bidirectional associative memory neural networks is studied in this paper. Based on the fractional Gronwall inequality with time delay, a suf-ficient condition for the finite-time stability of fractional bidirectional associative memory neural networks with time delays is obtained, which reduces the conservatism of the existing criteria. At last, two examples are given to confirm the validity and feasibility of the main results.
文章引用：罗雪梅, 李进东. 分数阶时滞的BAM神经网络的有限时间稳定性[J]. 应用数学进展, 2023, 12(6): 2677-2687. https://doi.org/10.12677/AAM.2023.126270

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