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Chin. Phys. B, 2018, Vol. 27(1): 010202    DOI: 10.1088/1674-1056/27/1/010202
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Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control

Yangui Xi(喜彦贵), Yongguang Yu(于永光), Shuo Zhang(张硕), Xudong Hai(海旭东)
Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China
Abstract  The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural networks. Then a robust control law is designed to ensure the occurrence of the sliding motion for stabilization of the fractional-order Hopfield neural networks. Besides, for the unknown parameters of the fractional-order Hopfield neural networks, some estimations are made. Based on the fractional-order Lyapunov theory, the finite-time stability of the sliding surface to origin is proved well. Finally, a typical example of three-dimensional uncertain fractional-order Hopfield neural networks is employed to demonstrate the validity of the proposed method.
Keywords:  fractional-order neural networks      finite-time      sliding mode control      parameters estimation  
Received:  05 May 2017      Revised:  25 September 2017      Accepted manuscript online: 
PACS:  02.30.Yy (Control theory)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11371049 and 61772063) and the Fundamental Research Funds for the Central Universities, China (Grant No. 2016JBM070).
Corresponding Authors:  Yongguang Yu     E-mail:

Cite this article: 

Yangui Xi(喜彦贵), Yongguang Yu(于永光), Shuo Zhang(张硕), Xudong Hai(海旭东) Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control 2018 Chin. Phys. B 27 010202

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