An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach
In this brief, we propose a class of generalized memristor-based neural networks with nonlinear coupling. Based on the set-valued mapping theory, novel Lyapunov indefinite derivative and Memristor theory, the coupled memristor-based neural networks (CMNNs) can achieve fixed-time stabilization (FTS)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-03-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023123?viewType=HTML |
| _version_ | 1797831743250628608 |
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| author | Chao Yang Juntao Wu Zhengyang Qiao |
| author_facet | Chao Yang Juntao Wu Zhengyang Qiao |
| author_sort | Chao Yang |
| collection | DOAJ |
| description | In this brief, we propose a class of generalized memristor-based neural networks with nonlinear coupling. Based on the set-valued mapping theory, novel Lyapunov indefinite derivative and Memristor theory, the coupled memristor-based neural networks (CMNNs) can achieve fixed-time stabilization (FTS) by designing a proper pinning controller, which randomly controls a small number of neuron nodes. Different from the traditional Lyapunov method, this paper uses the implementation method of indefinite derivative to deal with the non-autonomous neural network system with nonlinear coupling topology between different neurons. The system can obtain stabilization in a fixed time and requires fewer conditions. Moreover, the fixed stable setting time estimation of the system is given through a few conditions, which can eliminate the dependence on the initial value. Finally, we give two numerical examples to verify the correctness of our results. |
| first_indexed | 2024-04-09T13:57:38Z |
| format | Article |
| id | doaj.art-4171234128f04f3db29c1adf89ce2c7d |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-04-09T13:57:38Z |
| publishDate | 2023-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-4171234128f04f3db29c1adf89ce2c7d2023-05-08T01:08:17ZengAIMS PressElectronic Research Archive2688-15942023-03-013152428244610.3934/era.2023123An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approachChao Yang0Juntao Wu1Zhengyang Qiao21. Department of Mathematics and Computer Science, Changsha University, Changsha 410002, China2. Department of Mathematics, National University of Defense Technology, Changsha 410073, China2. Department of Mathematics, National University of Defense Technology, Changsha 410073, ChinaIn this brief, we propose a class of generalized memristor-based neural networks with nonlinear coupling. Based on the set-valued mapping theory, novel Lyapunov indefinite derivative and Memristor theory, the coupled memristor-based neural networks (CMNNs) can achieve fixed-time stabilization (FTS) by designing a proper pinning controller, which randomly controls a small number of neuron nodes. Different from the traditional Lyapunov method, this paper uses the implementation method of indefinite derivative to deal with the non-autonomous neural network system with nonlinear coupling topology between different neurons. The system can obtain stabilization in a fixed time and requires fewer conditions. Moreover, the fixed stable setting time estimation of the system is given through a few conditions, which can eliminate the dependence on the initial value. Finally, we give two numerical examples to verify the correctness of our results.https://www.aimspress.com/article/doi/10.3934/era.2023123?viewType=HTMLfixed-time stabilizationmemristornonlinear couplingindefinite derivative |
| spellingShingle | Chao Yang Juntao Wu Zhengyang Qiao An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach Electronic Research Archive fixed-time stabilization memristor nonlinear coupling indefinite derivative |
| title | An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach |
| title_full | An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach |
| title_fullStr | An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach |
| title_full_unstemmed | An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach |
| title_short | An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach |
| title_sort | improved fixed time stabilization problem of delayed coupled memristor based neural networks with pinning control and indefinite derivative approach |
| topic | fixed-time stabilization memristor nonlinear coupling indefinite derivative |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023123?viewType=HTML |
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