Positivity and semi-global polynomial stability of high-order Cohen–Grossberg BAM neural networks with multiple proportional delays

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2024-09-24 DOI:10.1016/j.ins.2024.121512

Yantao Wang , Xiaona Yang , Xi Chen , Chunyan Liu

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引用次数: 0

Abstract

In this paper, we study positivity and semi-global polynomial stability (PS) of higher-order Cohen-Grossberg BAM neural networks with multiple proportional time delays. The proportional delays considered here are unbounded and time-varying, differing from constant, bounded, and distributional time delays. The system model cannot be represented using vector and matrices, making certain approaches within the vector-matrix framework unsuitable for applying. To address this limitation, a direct method based on the solution of the system is proposed to provide sufficient conditions guaranteeing the positivity and semi-global polynomial stability (PS) of the model under consideration. Furthermore, the direct method is applied to establish global PS conditions for BAM neural networks with multiple proportional delays. The obtained conditions contain only a few simple linear scalar inequalities that are easily solved. The applicability of the obtained PS conditions is verified by two numerical examples, and the solution of a linear programming problem is also obtained based on these theoretical results. Notably, this method can be applied to many system models with proportional delays after minor modifications.

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具有多比例延迟的高阶科恩-格罗斯伯格 BAM 神经网络的正向性和半全局多项式稳定性

本文研究了具有多比例时间延迟的高阶 Cohen-Grossberg BAM 神经网络的正相关性和半全局多项式稳定性（PS）。本文考虑的比例延迟是无界和时变的，不同于恒定、有界和分布式时间延迟。系统模型无法用向量和矩阵表示，因此向量矩阵框架内的某些方法不适合应用。为了解决这一局限性，我们提出了一种基于系统解的直接方法，以提供充分条件，保证所考虑模型的正向性和半全局多项式稳定性（PS）。此外，该直接方法还被用于建立具有多比例延迟的 BAM 神经网络的全局 PS 条件。所获得的条件只包含几个简单的线性标量不等式，易于求解。通过两个数值实例验证了所获 PS 条件的适用性，并基于这些理论结果获得了线性规划问题的解。值得注意的是，在稍作修改后，这种方法可应用于许多具有比例延迟的系统模型。

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

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.