Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect
The dynamic behavior of memristive neural networks (MNNs), including synchronization, effectively keeps the robotic stability against numerous uncertainties from the mimic of the human brain. However, it is challenging to perform projective quasi-synchronization of coupled MNNs with low-consumer con...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2022.985312/full |
| _version_ | 1798003299503308800 |
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| author | Manman Yuan Manman Yuan Manman Yuan Xiong Luo Xiong Luo Xiong Luo Jun Hu Songxin Wang |
| author_facet | Manman Yuan Manman Yuan Manman Yuan Xiong Luo Xiong Luo Xiong Luo Jun Hu Songxin Wang |
| author_sort | Manman Yuan |
| collection | DOAJ |
| description | The dynamic behavior of memristive neural networks (MNNs), including synchronization, effectively keeps the robotic stability against numerous uncertainties from the mimic of the human brain. However, it is challenging to perform projective quasi-synchronization of coupled MNNs with low-consumer control devices. This is partly because complete synchronization is difficult to realize under various projective factors and parameter mismatch. This article aims to investigate projective quasi-synchronization from the perspective of the controller. Here, two approaches are considered to find the event-triggered scheme for lag synchronization of coupled MNNs. In the first approach, the projective quasi-synchronization issue is formulated for coupled MNNs for the first time, where the networks are combined with time-varying delays and uncertainties under the constraints imposed by the frequency of controller updates within limited system communication resources. It is shown that our methods can avoid the Zeno-behavior under the newly determined triggered functions. In the second approach, following classical methods, a novel projective quasi-synchronization criterion that combines the nonlinear property of the memristor and the framework of Lyapunov-Krasovskii functional (LKF) is proposed. Simulation results indicate that the proposed two approaches are useful for coupled MNNs, and they have less control cost for different types of quasi-synchronization. |
| first_indexed | 2024-04-11T12:05:34Z |
| format | Article |
| id | doaj.art-89be823f8ae94ee1af5b015aa6df2217 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-04-11T12:05:34Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-89be823f8ae94ee1af5b015aa6df22172022-12-22T04:24:44ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182022-09-011610.3389/fnbot.2022.985312985312Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effectManman Yuan0Manman Yuan1Manman Yuan2Xiong Luo3Xiong Luo4Xiong Luo5Jun Hu6Songxin Wang7School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaShunde Graduate School, University of Science and Technology Beijing, Foshan, ChinaBeijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaShunde Graduate School, University of Science and Technology Beijing, Foshan, ChinaBeijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, ChinaSchool of Economics and Management, Fuzhou University, Fuzhou, ChinaSchool of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, ChinaThe dynamic behavior of memristive neural networks (MNNs), including synchronization, effectively keeps the robotic stability against numerous uncertainties from the mimic of the human brain. However, it is challenging to perform projective quasi-synchronization of coupled MNNs with low-consumer control devices. This is partly because complete synchronization is difficult to realize under various projective factors and parameter mismatch. This article aims to investigate projective quasi-synchronization from the perspective of the controller. Here, two approaches are considered to find the event-triggered scheme for lag synchronization of coupled MNNs. In the first approach, the projective quasi-synchronization issue is formulated for coupled MNNs for the first time, where the networks are combined with time-varying delays and uncertainties under the constraints imposed by the frequency of controller updates within limited system communication resources. It is shown that our methods can avoid the Zeno-behavior under the newly determined triggered functions. In the second approach, following classical methods, a novel projective quasi-synchronization criterion that combines the nonlinear property of the memristor and the framework of Lyapunov-Krasovskii functional (LKF) is proposed. Simulation results indicate that the proposed two approaches are useful for coupled MNNs, and they have less control cost for different types of quasi-synchronization.https://www.frontiersin.org/articles/10.3389/fnbot.2022.985312/fullevent-triggeredmemristorcoupled neural networksprojective quasi-synchronizationuncertainties |
| spellingShingle | Manman Yuan Manman Yuan Manman Yuan Xiong Luo Xiong Luo Xiong Luo Jun Hu Songxin Wang Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect Frontiers in Neurorobotics event-triggered memristor coupled neural networks projective quasi-synchronization uncertainties |
| title | Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect |
| title_full | Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect |
| title_fullStr | Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect |
| title_full_unstemmed | Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect |
| title_short | Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect |
| title_sort | projective quasi synchronization of coupled memristive neural networks with uncertainties and impulsive effect |
| topic | event-triggered memristor coupled neural networks projective quasi-synchronization uncertainties |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2022.985312/full |
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