Exponential Synchronization of Stochastic Memristive Recurrent Neural Networks Under Alternate State Feedback Control

  • Xiaofan Li
  • Jian-an Fang
  • Huiyuan Li
Regular Papers Control Theory and Applications


This paper solves the exponential synchronization problem of two memristive recurrent neural networks with both stochastic disturbance and time-varying delays via periodically alternate state feedback control. First, a periodically alternate state feedback control rule is designed. Then, on the basis of the Lyapunov stability theory, some novel sufficient conditions guaranteeing exponential synchronization of drive-response stochastic memristive recurrent neural networks via periodically alternate state feedback control are derived. In contrast to some previous works about synchronization of memristive recurrent neural networks, the obtained results in this paper are not difficult to be validated, and complement, extend and generalize the earlier papers. Lastly, an illustrative example is provided to indicate the effectiveness and applicability of the obtained theoretical results.


Exponential synchronization memristor-based recurrent neural networks stochastic perturbations alternate control 


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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical EngineeringYancheng Institute of TechnologyYanchengChina
  2. 2.College of Information Science and TechnologyDonghua UniversityShanghaiChina

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