Dissipativity Analysis of Memristive Inertial Competitive Neural Networks with Mixed Delays

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

Neural Processing Letters Pub Date : 2024-04-20 DOI:10.1007/s11063-024-11610-3

Jin Yang, Jigui Jian

引用次数: 0

Abstract

Without altering the inertial system into the two first-order differential systems, this paper primarily works over the global exponential dissipativity (GED) of memristive inertial competitive neural networks (MICNNs) with mixed delays. For this purpose, a novel differential inequality is primarily established around the discussed system. Then, by applying the founded inequality and constructing some novel Lyapunov functionals, the GED criteria in the algebraic form and the linear matrix inequality (LMI) form are given, respectively. Furthermore, the estimation of the global exponential attractive set (GEAS) is furnished. Finally, a specific illustrative example is analyzed to check the correctness and feasibility of the obtained findings.

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具有混合延迟的膜惯性竞争神经网络的离散性分析

在不改变惯性系统为两个一阶微分系统的情况下，本文主要研究具有混合延迟的记忆惯性竞争神经网络（MICNN）的全局指数消散性（GED）。为此，本文主要围绕所讨论的系统建立了一个新的微分不等式。然后，通过应用所建立的不等式和构建一些新的 Lyapunov 函数，分别给出了代数形式和线性矩阵不等式（LMI）形式的 GED 标准。此外，还提供了全局指数吸引集（GEAS）的估计。最后，分析了一个具体的示例，以检验所得结论的正确性和可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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