A New Approach to Stability Analysis for Stochastic Hopfield Neural Networks With Time Delays

• 成果类型: Article
• 署名作者: Lv, Xiang
• 署名单位: Shanghai Normal University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3120682
• 发表日期: 2022
• 页码: 5278-5288
• 关键词: neurons; dynamical systems; DELAYS; Biological neural networks; Differential equations; Stochastic processes; Stability criteria; Random dynamical systems; STABILITY; stationary solutions; stochastic delay neural networks

摘要

This article is devoted to the existence and the global stability of stationary solutions for stochastic Hopfield neural networks with time delays and additive white noises. Using the method of random dynamical systems, we present a new approach to guarantee that the infinite-dimensional stochastic flow generated by stochastic delay differential equations admits a globally attracting random equilibrium in the state-space of continuous functions. An example is given to illustrate the effectiveness of our results, and the forward trajectory synchronization will occur.