This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p-norm (p≥ 2). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M-matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

中文翻译：

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具有多个时变延迟的双向联想记忆神经网络的全局稳定性

本文研究了具有离散和分布式时变延迟 (DBAMNN) 的双向联想记忆神经网络的全局稳定性。通过采用比较策略和不等式技术，底层 DBAMNN 的全局渐近稳定性 (GAS) 和全局指数稳定性 (GES) 在 p 范数 (p≥ 2) 方面受到关注。同时，还根据 1-范数分析了所解决的 DBAMNN 的 GES。当忽略分布式时间延迟时，相应的双向联想记忆神经网络的 GES 呈现为 M 矩阵，其中包括某些现有结果作为特例。最后提供了两个例子来证实理论的有效性。