ABSTRACT

In this paper, an impulsive Cohen-Grossberg bidirectional associative neural network with both time-varying and distributed delays is examined. Novel sufficient conditions for deriving stability with a desired rate, including the exponential one, are obtained. We consider a large class of admissible kernels encompassing the existing ones. Our findings cover the existing stability results in the literature. Finally, a numerical example is given for the validation of the theoretical outcomes.

中文翻译：

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延迟脉冲 Cohen-Grossberg BAM 神经网络系统的稳定性

摘要

在本文中，研究了具有时变和分布式延迟的脉冲 Cohen-Grossberg 双向关联神经网络。获得了以所需速率（包括指数速率）导出稳定性的新颖充分条件。我们考虑一大类包含现有内核的可接受内核。我们的研究结果涵盖了文献中现有的稳定性结果。最后给出了数值算例来验证理论结果。