2021-IEEE TNNLS-Synchronization of Generally Uncertain Markovian.pdf (4.58 MB)
Synchronization of generally uncertain Markovian inertial neural networks with random connection weight strengths and image encryption application
journal contribution
posted on 2021-12-17, 10:17 authored by Junyi Wang, Zewen Ji, Huaguang Zhang, Zhanshan Wang, Qinggang MengQinggang MengThis 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 SystemsVolume
34Issue
9Pages
5911 - 5925Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher 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
2021-11-16Publication date
2021-12-15Copyright date
2021ISSN
2162-237XeISSN
2162-2388Publisher version
Language
- en
Depositor
Prof Qinggang Meng. Deposit date: 16 December 2021Usage metrics
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