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

Complexity

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

Philosophy

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