Exponential stability of complex-valued memristor-based neural networks with time-varying delays

Yanchao Shi, Jinde Cao and Guanrong Chen

Applied Mathematics and Computation, 2017, vol. 313, issue C, 222-234

Abstract: In this paper, we propose a new type of complex-valued memristor-based neural networks with time-varying delays and discuss their exponential stability. Firstly, by using a matrix measure method, the Halanay inequality and some analytic techniques, we derive a sufficient condition for the global exponential stability of this type of neural networks. Then, we build a Lyapunov functional and utilize the Halanay inequality to establish several criteria for the exponential stability of such networks with time-varying delays. Finally, we show two numerical simulations to demonstrate the theoretical results.

Keywords: Memristor-based neural network; Complex-valued network; Matrix measure; Lyapunov–Krasovskii functional; Exponential stability (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:313:y:2017:i:c:p:222-234

DOI: 10.1016/j.amc.2017.05.078

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