New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control
Joint Authors
Fang, Jian-an
Li, Xiaofan
Ge, Yuan
Liu, Hongjian
Li, Huiyuan
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper addresses the synchronization issue for the drive-response fractional-order memristor‐based neural networks (FOMNNs) via state feedback control.
To achieve the synchronization for considered drive-response FOMNNs, two feedback controllers are introduced.
Then, by adopting nonsmooth analysis, fractional Lyapunov’s direct method, Young inequality, and fractional-order differential inclusions, several algebraic sufficient criteria are obtained for guaranteeing the synchronization of the drive-response FOMNNs.
Lastly, for illustrating the effectiveness of the obtained theoretical results, an example is given.
American Psychological Association (APA)
Li, Xiaofan& Ge, Yuan& Liu, Hongjian& Li, Huiyuan& Fang, Jian-an. 2020. New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control. Complexity،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1141016
Modern Language Association (MLA)
Li, Xiaofan…[et al.]. New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control. Complexity No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1141016
American Medical Association (AMA)
Li, Xiaofan& Ge, Yuan& Liu, Hongjian& Li, Huiyuan& Fang, Jian-an. New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control. Complexity. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1141016
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141016