带有比例时滞的分数阶双向联想记忆神经网络的有限时间稳定
Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Proportional Delay
DOI: 10.12677/AAM.2023.129391, PDF,   
作者: 张 杰：湖北汽车工业学院数理与光电工程学院，湖北 十堰
关键词: 比例时滞；Gronwall不等式；神经网络；有限时间稳定；Proportional Delay； Gronwall Inequality； Neural Networks； Finite-Time Stability
摘要: 本文研究了一类带有比例时滞的分数阶双向联想记忆神经网络的有限时间稳定性。基于一个新的广义Gronwall不等式，导出了阶数在0到1之间的分数阶系统的有限时间稳定的条件。最后通过数值仿真验证了所得条件的有效性。
Abstract: This paper focuses on the finite-time stability for a class of fractional-order bidirectional associative memory neural networks with proportional delay. Based on new generalized Gronwall inequality, a criterion is obtained to realize the finite-time stability of systems when the fractional order is be-tween 0 and 1. Finally, the effectiveness of our criteria is supported by a numerical example.
文章引用：张杰. 带有比例时滞的分数阶双向联想记忆神经网络的有限时间稳定[J]. 应用数学进展, 2023, 12(9): 3999-4005. https://doi.org/10.12677/AAM.2023.129391

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