2021-IEEE TNNLS-Synchronization of Generally Uncertain Markovian.pdf (4.58 MB)
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Synchronization of generally uncertain Markovian inertial neural networks with random connection weight strengths and image encryption application

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journal contribution
posted on 17.12.2021, 10:17 authored by Junyi Wang, Zewen Ji, Huaguang Zhang, Zhanshan Wang, Qinggang MengQinggang Meng
This article focuses on the synchronization problem of delayed inertial neural networks (INNs) with generally uncertain Markovian jumping and their applications in image encryption. The random connection weight strengths and generally uncertain Markovian are discussed in the INNs model. Compared with most existing INNs models that have constant connection weight strengths, our model is more practical because connection weight strengths of INNs may randomly vary due to the external and internal environment and human factor. The delay-range-dependent synchronization conditions (DRDSCs) could be obtained by adopting the delay-product-term Lyapunov-Krasovskii functional (DPTLKF) and higher order polynomial-based relaxed inequality (HOPRII). In addition, the desired controllers are obtained by solving a set of linear matrix inequalities. Finally, two examples are shown to demonstrate the effectiveness of the proposed results.

Funding

National Natural Science Foundation of China under Grant 61903075 and Grant U20A20197

Project of Liaoning Province Science and Technology Program under Grant 2019-KF-03-02

Fundamental Research Funds for the Central Universities under Grant N2026003

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Neural Networks and Learning Systems

Pages

1 - 15

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

16/11/2021

Publication date

2021-12-15

Copyright date

2021

ISSN

2162-237X

eISSN

2162-2388

Language

en

Depositor

Prof Qinggang Meng. Deposit date: 16 December 2021