Stability and passivity analysis of delayed neural networks via an improved matrix-valued polynomial inequality

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2024-08-21 DOI:10.1016/j.neunet.2024.106637

引用次数: 0

Abstract

The stability and passivity of delayed neural networks are addressed in this paper. A novel Lyapunov–Krasovskii functional (LKF) without multiple integrals is constructed. By using an improved matrix-valued polynomial inequality (MVPI), the previous constraint involving skew-symmetric matrices within the MVPI is removed. Then, the stability and passivity criteria for delayed neural networks that are less conservative than the existing ones are proposed. Finally, three examples are employed to demonstrate the meliority and feasibility of the obtained results.

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通过改进的矩阵值多项式不等式分析延迟神经网络的稳定性和被动性

本文探讨了延迟神经网络的稳定性和被动性。本文构建了一个新颖的无多重积分的 Lyapunov-Krasovskii 函数 (LKF)。通过使用改进的矩阵值多项式不等式（MVPI），消除了 MVPI 中以前涉及倾斜对称矩阵的约束。然后，提出了比现有标准更保守的延迟神经网络稳定性和被动性标准。最后，通过三个实例证明了所获结果的优越性和可行性。

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 Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.