非 Lipschitz 条件下具有 Lévy 噪声的脉冲随机中性神经网络的指数稳定性

IF 2.6 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Processing Letters Pub Date : 2024-06-28 DOI:10.1007/s11063-024-11663-4

Shuo Ma, Jiangman Li, Ruonan Liu, Qiang Li

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引用次数: 0

摘要

本文探讨了由勒维噪声驱动的随机冲动中性神经网络的指数稳定性问题。通过利用涉及中性时延分量的 Lyapunov-Krasovskii 函数、Lévy 过程的特性以及各种不等式方法，得到了非 Lipschitz 情况下的一些充分指数稳定性准则。此外，所取得的结果还取决于时延、噪声强度和脉冲因子。论文最后给出了两个数值模拟示例，以证明上述结果的有效性和可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Exponential Stability of Impulsive Stochastic Neutral Neural Networks with Lévy Noise Under Non-Lipschitz Conditions

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Exponential Stability of Impulsive Stochastic Neutral Neural Networks with Lévy Noise Under Non-Lipschitz Conditions

In this paper, the exponential stability issue of stochastic impulsive neutral neural networks driven by Lévy noise is explored. By resorting to the Lyapunov-Krasovskii function that involves neutral time-delay components, the properties of the Lévy process, as well as various inequality approaches, some sufficient exponential stability criteria in non-Lipschitz cases are obtained. Besides, the achieved results depend on the time-delay, noise intensity, and impulse factor. At the end of the paper, two numerical examples with simulations are presented to demonstrate the effectiveness and feasibility of the addressed results

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来源期刊

Neural Processing Letters 工程技术-计算机：人工智能

CiteScore

4.90

自引率

12.90%

发文量

392

审稿时长

2.8 months

期刊介绍： Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters