Global Anti-Synchronization of Complex-Valued Memristive Neural Networks With Time Delays
研究了一类复值忆阻神经网络的驱动-响应反同步问题，通过构造李雅普诺夫泛函和不等式技巧给出了保证反同步的充分条件，并用数值仿真验证了结果的有效性。
This paper formulates a class of complex-valued memristive neural networks as well as investigates the problem of anti-synchronization for complex-valued memristive neural networks. Under the concept of drive-response, several sufficient conditions for guaranteeing the anti-synchronization are given by employing suitable Lyapunov functional and some inequality techniques. The proposed results of this paper are less conservative than existing literatures due to the characteristics of memristive complex-valued neural networks. Moreover, the proposed results are easy to be validated with the parameters of system itself. Finally, two examples with numerical simulations are showed to demonstrate the efficiency of our theoretical results.