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受混合随机干扰的耦合切换神经网络的同步

Synchronization of coupled switched neural networks subject to hybrid stochastic disturbances.

作者信息

Long Han, Ci Jingxuan, Guo Zhenyuan, Wen Shiping, Huang Tingwen

机构信息

College of Science, National University of Defense Technology, Changsha 410073, China.

School of Mathematics, Hunan University, Changsha 410082, China.

出版信息

Neural Netw. 2023 Sep;166:459-470. doi: 10.1016/j.neunet.2023.07.045. Epub 2023 Aug 1.

Abstract

In this paper, the theoretical analysis on exponential synchronization of a class of coupled switched neural networks suffering from stochastic disturbances and impulses is presented. A control law is developed and two sets of sufficient conditions are derived for the synchronization of coupled switched neural networks. First, for desynchronizing stochastic impulses, the synchronization of coupled switched neural networks is analyzed by Lyapunov function method, the comparison principle and a impulsive delay differential inequality. Then, for general stochastic impulses, by partitioning impulse interval and using the convex combination technique, a set of sufficient condition on the basis of linear matrix inequalities (LMIs) is derived for the synchronization of coupled switched neural networks. Eventually, two numerical examples and a practical application are elaborated to illustrate the effectiveness of the theoretical results.

摘要

本文针对一类受随机干扰和脉冲影响的耦合切换神经网络的指数同步问题进行了理论分析。设计了一种控制律,并推导了两组耦合切换神经网络同步的充分条件。首先,针对失步随机脉冲,利用Lyapunov函数法、比较原理和一个脉冲时滞微分不等式对耦合切换神经网络的同步进行了分析。然后,针对一般随机脉冲,通过划分脉冲区间并运用凸组合技术,基于线性矩阵不等式(LMI)推导了一组耦合切换神经网络同步的充分条件。最后,给出了两个数值例子和一个实际应用,以说明理论结果的有效性。

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