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Global exponential stability analysis of discrete-time BAM neural networks with delays: A mathematical induction approach

机译：时滞离散BAM神经网络的全局指数稳定性分析：一种数学归纳法

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摘要

The problem of global exponential stability analysis for discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays is investigated. By using the mathematical induction method, a novel exponential stability criterion in the form of linear matrix inequalities is firstly established. Then stability criteria depending upon only the system parameters are derived, which can easily checked by using the standard toolbox software (e.g., MATLAB). The proposed approach is directly based on the definition of global exponential stability, and it does not involve the construct of any Lyapunov-Krasovskii functional or auxiliary function. For a class of special cases, it is theoretical proven that the less conservative stability criteria can be obtained by using the proposed approach than ones in literature. Moreover, several numerical examples are also provided to demonstrate the effectiveness of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.
机译：研究了具有时变时滞的离散时间双向联想记忆（BAM）神经网络的全局指数稳定性分析问题。通过数学归纳法，首先建立了线性矩阵不等式形式的新型指数稳定性判据。然后仅根据系统参数得出稳定性标准，可以使用标准工具箱软件（例如MATLAB）轻松检查其稳定性。所提出的方法直接基于全局指数稳定性的定义，并且不涉及任何Lyapunov-Krasovskii函数或辅助函数的构造。对于一类特殊情况，理论上证明，与文献中的方法相比，使用所提出的方法可以获得较保守的稳定性标准。此外，还提供了几个数值示例来证明所提出方法的有效性。 （C）2019 Elsevier B.V.保留所有权利。

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