School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
Neural Netw. 2017 Dec;96:115-127. doi: 10.1016/j.neunet.2017.09.008. Epub 2017 Sep 14.
This paper focuses on the dynamical property of a class of memristor-based complex-valued neural networks (MCVNNs) with time delays. By constructing the appropriate Lyapunov functional and utilizing the inequality technique, sufficient conditions are proposed to guarantee exponential synchronization of the coupled systems based on drive-response concept. The proposed results are very easy to verify, and they also extend some previous related works on memristor-based real-valued neural networks. Meanwhile, the obtained sufficient conditions of this paper may be conducive to qualitative analysis of some complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of our theoretical results.
本文研究了一类具有时滞的基于忆阻器的复值神经网络（MCVNNs）的动态特性。通过构造适当的李雅普诺夫泛函，并利用不等式技术，基于驱动-响应概念，提出了保证耦合系统指数同步的充分条件。所提出的结果非常易于验证，并且扩展了一些以前关于基于忆阻器的实值神经网络的相关工作。同时，本文得到的充分条件也有利于对一些复值非线性时滞系统的定性分析。通过一个数值例子验证了我们理论结果的有效性。
