时变时滞和分布时滞惯性神经网络全局指数稳定性的新发现

IF 1.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-03-08 DOI:10.1080/0952813X.2021.1883744

Qian Cao, Guoqiu Wang

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引用次数: 0

摘要

本文研究了具有时变时滞和分布时滞的惯性神经网络。利用不等式技术和Lyapunov函数方法，给出了一个新的保证寻址网络周期解存在性和指数稳定性的充分条件。所得到的结果补充了一些先前关于具有时变延迟的惯性神经网络的周期解的出版物。计算机模拟验证了推导的分析结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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New findings on global exponential stability of inertial neural networks with both time-varying and distributed delays

ABSTRACT In this manuscript, inertial neural networks with both time- varying and distributed delays are studied. Applying inequality techniques and Lyapunov function approach, a new sufficient condition that guarantees the existence and exponential stability of periodic solutions for the addressed networks is presented. The obtained results supplement some earlier publications that deal with the periodic solutions of inertial neural networks with time- varying delays. Computer simulations are displayed to check the derived analytical results.

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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