Abstract
In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.
| Original language | English |
|---|---|
| Article number | 8515236 |
| Pages (from-to) | 1841-1853 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 30 |
| Issue number | 6 |
| Early online date | 30 Oct 2018 |
| DOIs | |
| Publication status | Published - Jun 2019 |
| Externally published | Yes |
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 61873002, Grant 61703004, Grant 61833005, Grant 61860206008, and Grant 61773081, in part by the China Postdoctoral Science Foundation under Grant 2018M632206, in part by the National Natural Science Foundation of Anhui Province under Grant 1708085MF165 and Grant 1808085QA18, in part by the Technology Transformation Program of Chongqing Higher Education University under Grant KJZH17102, in part by the National Priority Research Project through Qatar National Research Fund under Grant NPRP 9 166-1-031, and in part by the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant BM2017002.
Keywords
- Dissipative synchronization
- gain-scheduled control
- Markovian jump memristive neural networks (MJMNNs)
- time-varying delays (TVDs)