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Global synchronization in finite time for fractional-order neural networks with discontinuous activations and time delays.
Peng, Xiao; Wu, Huaiqin; Song, Ka; Shi, Jiaxin.
Affiliation
  • Peng X; School of Science, Yanshan University, Qinhuangdao 066001, China.
  • Wu H; School of Science, Yanshan University, Qinhuangdao 066001, China. Electronic address: huaiqinwu@ysu.edu.cn.
  • Song K; School of Science, Yanshan University, Qinhuangdao 066001, China.
  • Shi J; School of Science, Yanshan University, Qinhuangdao 066001, China.
Neural Netw ; 94: 46-54, 2017 Oct.
Article de En | MEDLINE | ID: mdl-28750347
ABSTRACT
This paper is concerned with the global Mittag-Leffler synchronization and the synchronization in finite time for fractional-order neural networks (FNNs) with discontinuous activations and time delays. Firstly, the properties with respect to Mittag-Leffler convergence and convergence in finite time, which play a critical role in the investigation of the global synchronization of FNNs, are developed, respectively. Secondly, the novel state-feedback controller, which includes time delays and discontinuous factors, is designed to realize the synchronization goal. By applying the fractional differential inclusion theory, inequality analysis technique and the proposed convergence properties, the sufficient conditions to achieve the global Mittag-Leffler synchronization and the synchronization in finite time are addressed in terms of linear matrix inequalities (LMIs). In addition, the upper bound of the setting time of the global synchronization in finite time is explicitly evaluated. Finally, two examples are given to demonstrate the validity of the proposed design method and theoretical results.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Type d'étude: Prognostic_studies Langue: En Journal: Neural Netw Sujet du journal: NEUROLOGIA Année: 2017 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Type d'étude: Prognostic_studies Langue: En Journal: Neural Netw Sujet du journal: NEUROLOGIA Année: 2017 Type de document: Article Pays d'affiliation: Chine
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