Stochastically exponential synchronization for Markov jump neural networks with time-varying delays via event-triggered control scheme

Advances in Difference Equations

article

Liu, Xiaoman¹  Zhang, Haiyang¹  Yang, Jun²  Chen, Hao² 

[1] School of Mathematics and Computer Science, Yunnan Minzu University;College of Electrical and Information Engineering, Southwest University for Nationalities

关键词: Stochastically exponential synchronization;    Markov jump neural networks;    Time-varying delays;    Event-triggered control scheme;    Reciprocally convex combination inequality;   

DOI  :  10.1186/s13662-020-03109-7

学科分类：航空航天科学

来源: SpringerOpen

【 摘 要 】

This paper focuses on the stochastically exponential synchronization problem for one class of neural networks with time-varying delays (TDs) and Markov jump parameters (MJPs). To derive a tighter bound of reciprocally convex quadratic terms, we provide an improved reciprocally convex combination inequality (RCCI), which includes some existing ones as its particular cases. We construct an eligible stochastic Lyapunov–Krasovskii functional to capture more information about TDs, triggering signals, and MJPs. Based on a well-designed event-triggered control scheme, we derive several novel stability criteria for the underlying systems by employing the new RCCI and other analytical techniques. Finally, we present two numerical examples to show the validity of our methods.

【 授权许可】

CC BY