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Chang Q, Park JH, Yang Y. The Optimization of Control Parameters: Finite-Time Bipartite Synchronization of Memristive Neural Networks With Multiple Time Delays via Saturation Function. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:7861-7872. [PMID: 35139029 DOI: 10.1109/tnnls.2022.3146832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This article studies the memristive neural networks with multiple time delays (MNNsMTDs). The topology of networks is signed, which contains both cooperative and competitive relationships. Two controllers without time delays are designed to achieve finite-time bipartite synchronization (FTBS) and practical FTBS (PFTBS) of MNNsMTDs. A novel controller with a saturation function rather than a sign function is proposed to avoid chattering. Along with the Lyapunov function method, some mathematical techniques, and scaling inequalities, some sufficient conditions for FTBS and PFTBS of MNNsMTDs are attained. Besides, this article also concerns fixed-time bipartite synchronization (FXBS) and practical FXBS (PFXBS) of MNNsMTDs. An optimization model is designed to obtain some optimal control parameters. An algorithm based on particle swarm optimization (PSO) is provided to solve this model. Some numerical examples are included to demonstrate the correctness and applicability of the approaches.
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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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Wei R, Cao J. Prespecified-time bipartite synchronization of coupled reaction-diffusion memristive neural networks with competitive interactions. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12814-12832. [PMID: 36654023 DOI: 10.3934/mbe.2022598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In this paper, we investigate the prespecified-time bipartite synchronization (PTBS) of coupled reaction-diffusion memristive neural networks (CRDMNNs) with both competitive and cooperative interactions. Two types of bipartite synchronization are considered: leaderless PTBS and leader-following PTBS. With the help of a structural balance condition, the criteria for PTBS for CRDMNNs are derived by designing suitable Lyapunov functionals and novel control protocols. Different from the traditional finite-time or fixed-time synchronization, the settling time obtained in this paper is independent of control gains and initial values, which can be pre-set according to the task requirements. Lastly, numerical simulations are given to verify the obtained results.
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Affiliation(s)
- Ruoyu Wei
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea
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Event-triggered bipartite synchronization of coupled multi-order fractional neural networks. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Zou Y, Su H, Tang R, Yang X. Finite-time bipartite synchronization of switched competitive neural networks with time delay via quantized control. ISA TRANSACTIONS 2022; 125:156-165. [PMID: 34167820 DOI: 10.1016/j.isatra.2021.06.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 06/14/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
This article tackles the finite-time bipartite synchronization (FTBS) of coupled competitive neural networks (CNNs) with switching parameters and time delay. Quantized control is utilized to achieve the FTBS at a small control cost and with limited channel resources. Since the effects of the time delay and switching parameters, traditional finite-time techniques cannot be directly utilized to the FTBS. By constructing a novel multiple Lyapunov functional (MLF), a sufficient criterion formulated by linear programming (LP) is established for the FTBS and the estimation of the settling time. To further improve the accuracy of the settling time, another MLF is designed by dividing the dwell time. With the aid of convex combination, a new LP is provided, which removes the requirement that the increment coefficient of the MLF at switching instants has to be larger than 1. In addition, to obtain the more precise settling time, an optimal algorithm is provided. Two numerical examples are put forward to demonstrate the reasonableness of the theoretical analysis.
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Affiliation(s)
- Yi Zou
- School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China.
| | - Housheng Su
- Key Laboratory of Imaging Processing and Intelligence Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Rongqiang Tang
- Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha 410114, China.
| | - Xinsong Yang
- College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
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Bipartite leader-following synchronization of delayed incommensurate fractional-order memristor-based neural networks under signed digraph via adaptive strategy. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Zhong X, Ren J, Gao Y. Passivity-based Bipartite Synchronization of Coupled Delayed Inertial Neural Networks via Non-reduced Order Method. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10839-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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8
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Preassigned-Time Synchronization of Delayed Fuzzy Cellular Neural Networks with Discontinuous Activations. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10808-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Samanta S, Kumar Dubey V, Das K. Coopetition bunch graphs: Competition and cooperation on COVID19 research. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Abstract
In this paper, several multi-layer-coupled star-composed networks with similar symmetrical structures are defined by using the theory of graph operation. The supra-Laplacian matrix of the corresponding multi-layer networks is obtained according to the master stability equation (MSF). Two important indexes that reflect the synchronizability of these kinds of networks are derived in the case of bounded and unbounded synchronized regions. The relationships among the synchronizability, the number of layers, the length of the paths, the branchings, and the interlayer and intralayer coupling strengths in the two cases are studied. At the same time, the simulation experiments are carried out with the MATLAB software, and the simulated images of the two symmetrical structure networks’ synchronizability are compared. Finally, the factors affecting the synchronizability of multi-layer-coupled star-composed networks are found. On this basis, optimization schemes are given to improve the synchronizability of multi-layer-coupled star-composed networks and the influences of the number of central nodes on the networks’ synchronizability are further studied.
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Udhayakumar K, Rihan FA, Rakkiyappan R, Cao J. Fractional-order discontinuous systems with indefinite LKFs: An application to fractional-order neural networks with time delays. Neural Netw 2021; 145:319-330. [PMID: 34798343 DOI: 10.1016/j.neunet.2021.10.027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/16/2021] [Accepted: 10/29/2021] [Indexed: 11/18/2022]
Abstract
In this article, we discuss bipartite fixed-time synchronization for fractional-order signed neural networks with discontinuous activation patterns. The Filippov multi-map is used to convert the fixed-time stability of the fractional-order general solution into the zero solution of the fractional-order differential inclusions. On the Caputo fractional-order derivative, Lyapunov-Krasovskii functional is proved to possess the indefinite fractional derivatives for fixed-time stability of fragmentary discontinuous systems. Furthermore, the fixed-time stability of the fractional-order discontinuous system is achieved as well as an estimate of the new settling time.. The discontinuous controller is designed for the delayed fractional-order discontinuous signed neural networks with antagonistic interactions and new conditions for permanent fixed-time synchronization of these networks with antagonistic interactions are also provided, as well as the settling time for permanent fixed-time synchronization. Two numerical simulation results are presented to demonstrate the effectiveness of the main results.
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Affiliation(s)
- K Udhayakumar
- Department of Mathematics, Bharathiar University, Coimbatore 641 046, India; Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al-Ain, 15551, United Arab Emirates
| | - Fathalla A Rihan
- Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al-Ain, 15551, United Arab Emirates.
| | - R Rakkiyappan
- Department of Mathematics, Bharathiar University, Coimbatore 641 046, India
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China; Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea
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Xu D, Pang J, Su H. Bipartite synchronization of signed networks via aperiodically intermittent control based on discrete-time state observations. Neural Netw 2021; 144:307-319. [PMID: 34547669 DOI: 10.1016/j.neunet.2021.08.035] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 06/15/2021] [Accepted: 08/30/2021] [Indexed: 11/27/2022]
Abstract
In this paper, bipartite synchronization of signed networks with stochastic disturbances via aperiodically intermittent control is investigated. The aperiodically intermittent control presented is based on discrete-time state observations rather than continuous-time ones. To formulate signed networks and exhibit the competitive relation, a structurally balanced signed network is built and all the units are divided into two subcommunities. By employing Lyapunov method and graph theory, some sufficient conditions on bipartite synchronization are given. Meanwhile, when aperiodically intermittent control degenerates into periodically intermittent control and feedback control respectively, two corollaries are also provided to ensure the bipartite synchronization of the signed networks. Ultimately, two applications to coupled single-link robot arms and coupled oscillators are presented and corresponding numerical examples are respectively provided to verify the feasibility and effectiveness of the theoretical results.
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Affiliation(s)
- Dongsheng Xu
- Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai 264209, PR China
| | - Jiahuan Pang
- Department of Mechanical Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, PR China
| | - Huan Su
- Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai 264209, PR China.
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Miao B, Li X, Lou J, Lu J. Pinning bipartite synchronization for coupled reaction-diffusion neural networks with antagonistic interactions and switching topologies. Neural Netw 2021; 141:174-183. [PMID: 33906083 DOI: 10.1016/j.neunet.2021.04.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 04/05/2021] [Accepted: 04/05/2021] [Indexed: 10/21/2022]
Abstract
In this paper, the bipartite synchronization issue for a class of coupled reaction-diffusion networks with antagonistic interactions and switching topologies is investigated. First of all, by virtue of Lyapunov functional method and pinning control technique, we obtain some sufficient conditions which can guarantee that networks with signed graph topologies realize bipartite synchronization under any initial conditions and arbitrary switching signals. Secondly, for the general switching signal and periodic switching signal, a pinning controller that can ensure bipartite synchronization of reaction-diffusions networks is designed based on the obtained conditions. Meanwhile, a directed relationship between coupling strength and control gains is presented. Thirdly, numerical simulation is provided to demonstrate the correctness and validity of the derived theoretical results for reaction-diffusion systems. We briefly conclude our findings and future work.
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Affiliation(s)
- Baojun Miao
- School of Science, Xuchang University, Xuchang 461000, China
| | - Xuechen Li
- School of Science, Xuchang University, Xuchang 461000, China
| | - Jungang Lou
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, China.
| | - Jianquan Lu
- School of Mathematics, Southeast University, Nanjing 210096, China; College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China.
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