Synchronization of generally uncertain Markovian inertial neural networks with random connection weight strengths and image encryption application
journal contributionposted on 17.12.2021, 10:17 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.
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
- Computer Science