Global projective synchronization in finite time of nonidentical fractional-order neural networks based on sliding mode control strategy

In this paper, a novel fractional-order sliding mode control strategy is introduced to synchronize two nonidentical fractional order neural networks (FNNs) in finite time. Firstly, by applying fractional-order calculation, the properties of the global asymptotical stability and convergence in finite...

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Published inNeurocomputing (Amsterdam) Vol. 235; pp. 264 - 273
Main Authors Wu, Huaiqin, Wang, Lifei, Niu, Peifeng, Wang, Yu
Format Journal Article
LanguageEnglish
Published Elsevier B.V 26.04.2017
Subjects
Convergence in finite time
Fractional neural networks
Global asymptotical stability
Projective synchronization
Sliding surface
Convergence in finite time
Sliding surface
Fractional neural networks
Projective synchronization
Global asymptotical stability
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Abstract In this paper, a novel fractional-order sliding mode control strategy is introduced to synchronize two nonidentical fractional order neural networks (FNNs) in finite time. Firstly, by applying fractional-order calculation, the properties of the global asymptotical stability and convergence in finite time are developed for the fractional-order differential equation system. Secondly, based on the proposed principle of convergence in finite time, a new fractional-order sliding mode surface is presented and its global stability in finite time is analytically proved. In addition, an appropriate sliding mode controller is designed to drive the state trajectories of error system to the prescribed sliding surface in finite time and remain on it evermore. Meanwhile, by means of the fractional Lyapunov-like approach, the global projective synchronization condition is given, and the synchronization time is explicitly evaluated. As the special case, some sufficient conditions are given to guarantee global complete synchronization, anti-synchronization and stabilization of nonidentical FNNs. Finally, an example is given to demonstrate the validity of the proposed method.
AbstractList In this paper, a novel fractional-order sliding mode control strategy is introduced to synchronize two nonidentical fractional order neural networks (FNNs) in finite time. Firstly, by applying fractional-order calculation, the properties of the global asymptotical stability and convergence in finite time are developed for the fractional-order differential equation system. Secondly, based on the proposed principle of convergence in finite time, a new fractional-order sliding mode surface is presented and its global stability in finite time is analytically proved. In addition, an appropriate sliding mode controller is designed to drive the state trajectories of error system to the prescribed sliding surface in finite time and remain on it evermore. Meanwhile, by means of the fractional Lyapunov-like approach, the global projective synchronization condition is given, and the synchronization time is explicitly evaluated. As the special case, some sufficient conditions are given to guarantee global complete synchronization, anti-synchronization and stabilization of nonidentical FNNs. Finally, an example is given to demonstrate the validity of the proposed method.
Author Niu, Peifeng
Wang, Yu
Wang, Lifei
Wu, Huaiqin
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  organization: School of Science, Yanshan University, Qinhuangdao 066001, China
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Keywords Convergence in finite time
Sliding surface
Fractional neural networks
Projective synchronization
Global asymptotical stability
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SSID ssj0017129
Score 2.478295
Snippet In this paper, a novel fractional-order sliding mode control strategy is introduced to synchronize two nonidentical fractional order neural networks (FNNs) in...
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elsevier
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StartPage 264
SubjectTerms Convergence in finite time
Fractional neural networks
Global asymptotical stability
Projective synchronization
Sliding surface
Title Global projective synchronization in finite time of nonidentical fractional-order neural networks based on sliding mode control strategy
URI https://dx.doi.org/10.1016/j.neucom.2017.01.022
Volume 235
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