随机神经网络
数学
李雅普诺夫函数
控制理论(社会学)
随机微分方程
理论(学习稳定性)
国家(计算机科学)
随机过程
连续时间随机过程
人工神经网络
脉冲(物理)
应用数学
控制(管理)
计算机科学
循环神经网络
非线性系统
算法
人工智能
机器学习
统计
物理
量子力学
作者
Ning Zhang,Shijie Jiang,Wenxue Li
标识
DOI:10.1016/j.sysconle.2023.105494
摘要
In this paper, stability for state-dependent delayed complex networks under stochastic hybrid impulsive control is investigated. The impulses herein are stochastic and hybrid, that is, the impulsive gain at different impulsive moments is a sequence of random variables. The novel concept of average stochastic impulsive gain is proposed in this paper to deal with stochastic hybrid impulses. We also establish a new stochastic impulsive differential inequality with state-dependent delay. In the proceeding, combining the inequality with graph theory, stochastic analysis techniques and the Lyapunov method, stability criteria for the investigated networks are given. At the end, we apply the derived theoretical results to the special case of neural networks and the numerical analysis results illustrate the validity of the theoretical results.
科研通智能强力驱动
Strongly Powered by AbleSci AI