Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control

Neural Networks : the Official Journal of the International Neural Network Society
Shiju YangTingwen Huang

Abstract

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.

References

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Oct 6, 2012·Neural Networks : the Official Journal of the International Neural Network Society·Ailong Wu, Zhigang Zeng
Mar 25, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Chaojie LiTingwen Huang
Apr 8, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Guodong Zhang, Yi Shen
Dec 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Ailong Wu, Zhigang Zeng
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Jul 27, 2015·Neural Networks : the Official Journal of the International Neural Network Society·A ChandrasekarJinde Cao

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Citations

Jul 28, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Jinliang LiuShumin Fei
Sep 19, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Sanbo DingHuaguang Zhang

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