Global Anti-Synchronization of Complex-Valued Memristive Neural Networks With Time Delays

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Abstract
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.
Funding Information
  • Fundamental Research Funds for the Central Universities (2014XT02)
  • National Natural Science Foundation of China (61134012)
  • China Postdoctoral Science Foundation (2014M560459)

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