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Finite-time synchronization of memristor-based complex-valued neural networks with time delays
Affiliation:1. School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China;2. School of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China
Abstract:This paper deals with the problem of finite-time synchronization of memristor-based complex-valued neural networks (MCVNNs) with time delays. Based on the theory of differential inclusions with discontinuous right-hand side, we establish a new algebraic criterion of the finite-time synchronization of memristor-based complex-valued neural networks with time delays. The obtained theoretical results complement and improve some existing achievements in the real number field. Meanwhile, the obtained sufficient condition is conducive to qualitative analysis for some complex-valued nonlinear delayed systems. In the end, the conclusion is substantiated with an example of numerical simulation.
Keywords:Memristor  Finite-time synchronization  Complex-valued neural networks  Time delays
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