迟滞脉冲Cohen-Grossberg BAM神经网络系统的稳定性

IF 1.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-08-30 DOI:10.1080/0952813X.2021.1966840

Sakina Othmani, N. Tatar

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引用次数: 0

摘要

研究了一种具有时变时滞和分布时滞的脉冲Cohen-Grossberg双向关联神经网络。得到了具有期望速率(包括指数速率)的稳定性的新的充分条件。我们考虑一大类包含现有核的可容许核。我们的发现涵盖了文献中已有的稳定性结果。最后，通过数值算例对理论结果进行了验证。

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Stability for a retarded impulsive Cohen–Grossberg BAM neural network system

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.

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来源期刊

Journal of Experimental & Theoretical Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

6.10

自引率

4.50%

发文量

审稿时长

>12 weeks

期刊介绍： Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving