Event-triggered control for robust exponential synchronization of inertial memristive neural networks under parameter disturbance

Wei Yao; Chunhua Wang; Yichuang Sun; Shuqing Gong; Hairong Lin

doi:10.1016/j.neunet.2023.04.024

Event-triggered control for robust exponential synchronization of inertial memristive neural networks under parameter disturbance

Neural Netw. 2023 Jul:164:67-80. doi: 10.1016/j.neunet.2023.04.024. Epub 2023 Apr 26.

Authors

Wei Yao¹, Chunhua Wang², Yichuang Sun³, Shuqing Gong⁴, Hairong Lin²

Affiliations

¹ School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China; Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science and Technology, Changsha, 410114, China. Electronic address: yaowei@csust.edu.cn.
² College of Information Science and Engineering, Hunan University, Changsha, 410082, China.
³ School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK.
⁴ School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha 410114, China.

PMID: 37148609
DOI: 10.1016/j.neunet.2023.04.024

Abstract

Synchronization of memristive neural networks (MNNs) by using network control scheme has been widely and deeply studied. However, these researches are usually restricted to traditional continuous-time control methods for synchronization of the first-order MNNs. In this paper, we study the robust exponential synchronization of inertial memristive neural networks (IMNNs) with time-varying delays and parameter disturbance via event-triggered control (ETC) scheme. First, the delayed IMNNs with parameter disturbance are changed into first-order MNNs with parameter disturbance by constructing proper variable substitutions. Next, a kind of state feedback controller is designed to the response IMNN with parameter disturbance. Based on feedback controller, some ETC methods are provided to largely decrease the update times of controller. Then, some sufficient conditions are provided to realize robust exponential synchronization of delayed IMNNs with parameter disturbance via ETC scheme. Moreover, the Zeno behavior will not happen in all ETC conditions shown in this paper. Finally, numerical simulations are given to verify the advantages of the obtained results such as anti-interference performance and good reliability.

Keywords: Event-triggered control; Inertial memristive neural networks; Parameter disturbance; Robust exponential synchronization.

MeSH terms

Feedback
Neural Networks, Computer*
Reproducibility of Results
Time Factors