Point-sampled-data passivity stabilization of stochastic complex-valued memristor networks with multi-delays and reaction-diffusion term: A switching model approach. Issue 18 (December 2022)

• Record Type: Journal Article
• Authors: Wang, Juan; Wang, Zipeng; Shi, Kaibo; Jia, Fujin; Ding, Kui
• Is Part Of: Journal of the Franklin Institute. Volume 359:Issue 18(2022)
• Page Start: 11108
• Page End: 11134
• Publication Date: 2022-12
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2022.07.047
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper focuses on the stochastic passivity problem of stochastic memristor-based complex valued neural networks with two different types of time-delays and reaction-diffusion terms by sampled-data control strategy. Different from the existing sampled-data strategies, this paper develops spatial and temporal point sampling, namely, only a finite number of points in space or time are sampled. By introducing two different Lyapunov functional and employing techniques such as Wirtinger's integral inequality, Jensen's inequality and Young's inequality, etc., two different sufficient conditions for the stochastic passivity of the system are established. Prominently, the condition quantitatively reveals the relationship between the upper and lower bounds of the sampling interval at spatial and temporal points. Finally, a numerical example is given to verify the rationality of the proposed method. Notice, compared with a large number of results of real-valued reaction-diffusion neural networks, the research results of sampled-data controlled complex-valued reaction-diffusion neural networks have not appeared so far, and this work is the first attempt to fill in the gaps in this topic.