An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control

Mengzhuo Luo, Jun Cheng, Xinzhi Liu and Shouming Zhong

Applied Mathematics and Computation, 2019, vol. 344-345, 163-182

Abstract: This paper deals with the synchronization problem for a class of memristor-based coupled neural networks with mixed delays via some novel aperiodically intermittent control approaches with l different switched periods. Firstly, the control width, the non-control width and rates of control duration are different in each switched periods in this paper, hence, our intermittent control schemes are completely aperiodically case; Secondly, two different algorithms have been adopted to investigate the synchronization problem. Comparing with two different handing techniques, the main difference is focused on whether the rates of control duration are monotonic and whether the time-varying delays are derivative. Particularly, some strict assumptions between the time delay, control width and non-control width in the original references have been removed; Thirdly, by virtue of the Lyapunov stability theory and proposed control methods, several new criteria ensuring globally exponential synchronization of memristor-based coupled neural networks are obtained, and the new proposed results here are very easy to verify and also complement. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

Keywords: Memristor-based neural networks; Aperiodically intermittent control; Mixed delayed; l different switched periods Exponential synchronization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:344-345:y:2019:i::p:163-182

DOI: 10.1016/j.amc.2018.10.001

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