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Diao W, He W. Event-triggered protocol-based adaptive impulsive control for delayed chaotic neural networks. CHAOS (WOODBURY, N.Y.) 2024; 34:063132. [PMID: 38865097 DOI: 10.1063/5.0211621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024]
Abstract
This article focuses on the synchronization problem of delayed chaotic neural networks via adaptive impulsive control. An adaptive impulsive gain law in a discrete-time framework is designed. The delay is handled skillfully by using the Lyapunov-Razumikhin method. To improve the flexibility of impulsive control, an event-triggered impulsive strategy to determine when the impulsive instant happens is designed. Additionally, it is proved that the event-triggered impulsive sequence cannot result in the occurrence of Zeno behavior. Some criteria are derived to guarantee synchronization for delayed chaotic neural networks. Eventually, an illustrative example is presented to empirically validate the effectiveness of the suggested strategy.
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Affiliation(s)
- Weilu Diao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Wangli He
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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Xu Y, Yang C, Zhou L, Ma L, Zhu S. Adaptive event-triggered synchronization of neural networks under stochastic cyber-attacks with application to Chua's circuit. Neural Netw 2023; 166:11-21. [PMID: 37480766 DOI: 10.1016/j.neunet.2023.07.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/18/2023] [Accepted: 07/04/2023] [Indexed: 07/24/2023]
Abstract
This paper focuses on the synchronization control problem for neural networks (NNs) subject to stochastic cyber-attacks. Firstly, an adaptive event-triggered scheme (AETS) is adopted to improve the utilization rate of network resources, and an output feedback controller is constructed for improving the performance of the system subject to the conventional deception attack and accumulated dynamic cyber-attack. Secondly, the synchronization problem of master-slave NNs is transformed into the stability analysis problem of the synchronization error system. Thirdly, by constructing a customized Lyapunov-Krasovskii functional (LKF), the adaptive event-triggered output feedback controller is designed to ensure the synchronization error system is asymptotically stable with a given H∞ performance index. Lastly, in the simulation part, two examples, including Chua's circuit, illustrate the feasibility and universality of the related technologies in this paper.
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Affiliation(s)
- Yao Xu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, Xuzhou, 221116, China.
| | - Chunyu Yang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, Xuzhou, 221116, China.
| | - Linna Zhou
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, Xuzhou, 221116, China.
| | - Lei Ma
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, Xuzhou, 221116, China.
| | - Song Zhu
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
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Long H, Ci J, Guo Z, Wen S, Huang T. Synchronization of coupled switched neural networks subject to hybrid stochastic disturbances. Neural Netw 2023; 166:459-470. [PMID: 37574620 DOI: 10.1016/j.neunet.2023.07.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/29/2023] [Accepted: 07/27/2023] [Indexed: 08/15/2023]
Abstract
In this paper, the theoretical analysis on exponential synchronization of a class of coupled switched neural networks suffering from stochastic disturbances and impulses is presented. A control law is developed and two sets of sufficient conditions are derived for the synchronization of coupled switched neural networks. First, for desynchronizing stochastic impulses, the synchronization of coupled switched neural networks is analyzed by Lyapunov function method, the comparison principle and a impulsive delay differential inequality. Then, for general stochastic impulses, by partitioning impulse interval and using the convex combination technique, a set of sufficient condition on the basis of linear matrix inequalities (LMIs) is derived for the synchronization of coupled switched neural networks. Eventually, two numerical examples and a practical application are elaborated to illustrate the effectiveness of the theoretical results.
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Affiliation(s)
- Han Long
- College of Science, National University of Defense Technology, Changsha 410073, China.
| | - Jingxuan Ci
- School of Mathematics, Hunan University, Changsha 410082, China.
| | - Zhenyuan Guo
- School of Mathematics, Hunan University, Changsha 410082, China.
| | - Shiping Wen
- Centre for Artificial Intelligence, Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
| | - Tingwen Huang
- Science Program, Texas A&M University at Qatar, PO Box 23874, Doha, Qatar.
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Li JY, Huang YC, Rao HX, Xu Y, Lu R. Finite-time cluster synchronization for complex dynamical networks under FDI attack: A periodic control approach. Neural Netw 2023; 165:228-237. [PMID: 37307666 DOI: 10.1016/j.neunet.2023.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/05/2023] [Accepted: 04/10/2023] [Indexed: 06/14/2023]
Abstract
In this paper, the finite-time cluster synchronization problem is addressed for complex dynamical networks (CDNs) with cluster characteristics under false data injection (FDI) attacks. A type of FDI attack is taken into consideration to reflect the data manipulation that controllers in CDNs may suffer. In order to improve the synchronization effect while reducing the control cost, a new periodic secure control (PSC) strategy is proposed in which the set of pinning nodes changes periodically. The aim of this paper is to derive the gains of the periodic secure controller such that the synchronization error of the CDN remains at a certain threshold in finite time with the presence of external disturbances and false control signals simultaneously. Through considering the periodic characteristics of PSC, a sufficient condition is obtained to guarantee the desired cluster synchronization performance, based on which the gains of the periodic cluster synchronization controllers are acquired by resolving an optimization problem proposed in this paper. A numerical case is carried out to validate the cluster synchronization performance of the PSC strategy under cyber attacks.
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Affiliation(s)
- Jun-Yi Li
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China; Pazhou Lab, 510330, Guangzhou, China.
| | - Yang-Cheng Huang
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China.
| | - Hong-Xia Rao
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China.
| | - Yong Xu
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China.
| | - Renquan Lu
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China.
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A Unified Synchronization Criterion for Reaction-Diffusion Neural Networks with Time-Varying Impulsive Delays and System Delay. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10994-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zhang X, Li C, Li H, Cao Z. Mean-square stabilization of impulsive neural networks with mixed delays by non-fragile feedback involving random uncertainties. Neural Netw 2022; 154:469-480. [DOI: 10.1016/j.neunet.2022.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/19/2022] [Accepted: 07/07/2022] [Indexed: 10/16/2022]
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