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Finite-time decentralized event-triggered state estimation for coupled neural networks under unreliable Markovian network against mixed cyberattacks |
| Xiulin Wang(汪修林), Youzhi Cai(蔡有志), and Feng Li(李峰)† |
| School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243032, China |
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Abstract This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks, in which the Markov process is used to model the mixed cyberattacks. To optimize the utilization of channel resources, a decentralized event-triggered mechanism is adopted during the information transmission. By establishing the augmentation system and constructing the Lyapunov function, sufficient conditions are obtained for the system to be finite-time bounded and satisfy the $H_{\infty}$ performance index. Then, under these conditions, a suitable state estimator gain is obtained. Finally, the feasibility of the method is verified by a given illustrative example.
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Received: 17 July 2024
Revised: 04 September 2024
Accepted manuscript online: 24 September 2024
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PACS:
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02.30.Yy
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(Control theory)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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02.50.Ga
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(Markov processes)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 62303016), the Research and Development Project of Engineering Research Center of Biofilm Water Purification and Utilization Technology of the Ministry of Education of China (Grant No. BWPU2023ZY02), the University Synergy Innovation Program of Anhui Province, China (Grant No. GXXT-2023-020), and the Key Project of Natural Science Research in Universities of Anhui Province, China (Grant No. 2024AH050171). |
Corresponding Authors:
Feng Li
E-mail: fengli4131@gmail.com
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Cite this article:
Xiulin Wang(汪修林), Youzhi Cai(蔡有志), and Feng Li(李峰) Finite-time decentralized event-triggered state estimation for coupled neural networks under unreliable Markovian network against mixed cyberattacks 2024 Chin. Phys. B 33 110207
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