Impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks

IEEE Transactions on Neural Networks and Learning Systems
Wu-Hua ChenWei Xing Zheng

Abstract

This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.

References

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Citations

May 23, 2015·IEEE Transactions on Neural Networks and Learning Systems·Min XiaoJinde Cao
May 3, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Qiankun SongYurong Liu
Jan 27, 2016·IEEE Transactions on Neural Networks and Learning Systems·Wu-Hua ChenWei Xing Zheng
Feb 18, 2016·IEEE Transactions on Neural Networks and Learning Systems·Xinzhi LiuWei-Chau Xie
Aug 23, 2017·IEEE Transactions on Neural Networks and Learning Systems·Wei ZhuGang Feng
May 24, 2020·Neural Networks : the Official Journal of the International Neural Network Society·Deqiang OuyangHeng Tao Shen
Nov 8, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Peng WanShuang Jin

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