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Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach.
Neural Networks ( IF 7.8 ) Pub Date : 2020-06-26 , DOI: 10.1016/j.neunet.2020.06.020
Qiang Xiao 1 , Tingwen Huang 2
Affiliation  

This note introduces a unified matrix-measure concept to study the stability of a class of inertial neural networks with bounded time delays on time scales. The novel matrix-measure concept unifies the classic matrix-measure and the generalized matrix-measure concept. One sufficient global exponential stability criterion is obtained based on this key matrix-measure and no Lyapunov function is required. To make the stability performance better, another stability criterion in which more detailed information is involved has been acquired. The theoretical results in this note contain and extend some existing continuous-time and discrete-time works. A numerical example is given to show the validity of the results.



中文翻译:

时滞惯性神经网络的稳定性:统一的矩阵测度方法。

本文介绍了一个统一的矩阵测度概念,以研究一类在时标上具有有限时滞的惯性神经网络的稳定性。新颖的矩阵测度概念将经典的矩阵测度和广义的矩阵测度概念结合在一起。基于此关键矩阵测度,可以获得一个足够的全局指数稳定性准则,并且不需要Lyapunov函数。为了使稳定性更好,已经获取了涉及更多详细信息的另一种稳定性标准。本说明中的理论结果包含并扩展了一些现有的连续时间和离散时间工作。数值例子说明了结果的正确性。

更新日期:2020-06-28
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