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Zhou Y, Lv W, Tao J, Xu Y, Huang T, Rutkowski L. Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel. Neural Netw 2024; 169:485-495. [PMID: 37939537 DOI: 10.1016/j.neunet.2023.10.045] [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: 04/17/2023] [Revised: 09/27/2023] [Accepted: 10/29/2023] [Indexed: 11/10/2023]
Abstract
This work addresses the quasi-synchronization of delay master-slave BAM neural networks. To improve the utilization of channel bandwidth, a dynamic event-triggered impulsive mechanism is employed, in which data is transmitted only when a preset event-triggered mechanism or a forced impulse interval is satisfied. In addition, to guarantee the reliability of information transmission, a reliable redundant channel for BAM neural networks is adopted, whose transmission scheduling strategy is designed on the basis of the packet dropouts rate of the main communication channels. Further, an algorithm is employed to reduce the quasi-synchronization range of the error systems and the controllers are obtained. At last, a simulation result is shown to illustrate the effectiveness of the presented strategy.
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Affiliation(s)
- Yumei Zhou
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Weijun Lv
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Jie Tao
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Yong Xu
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Tingwen Huang
- Science Program, Texas A & M University at Qatar, Doha 23874, Qatar.
| | - Leszek Rutkowski
- Systems Research Institute of the Polish Academy of Sciences, 01-447 Warsaw, Poland; Institute of Computer Science, AGH University of Science and Technology in Kraków, 30-059 Kraków, Poland; Information Technology Institute, University of Social Sciences, 90-113 Łódź, Poland.
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2
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Zhang JR, Lu JG, Jin XC, Yang XY. Novel results on asymptotic stability and synchronization of fractional-order memristive neural networks with time delays: The 0<δ≤1 case. Neural Netw 2023; 167:680-691. [PMID: 37722271 DOI: 10.1016/j.neunet.2023.09.007] [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: 12/02/2022] [Revised: 07/14/2023] [Accepted: 09/04/2023] [Indexed: 09/20/2023]
Abstract
This paper investigates the asymptotic stability and synchronization of fractional-order (FO) memristive neural networks with time delays. Based on the FO comparison principle and inverse Laplace transform method, the novel sufficient conditions for the asymptotic stability of a FO nonlinear system are given. Then, based on the above conclusions, the sufficient conditions for the asymptotic stability and synchronization of FO memristive neural networks with time delays are investigated. The results in this paper have a wider coverage of situations and are more practical than the previous related results. Finally, the validity of the results is checked by two examples.
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Affiliation(s)
- Jia-Rui Zhang
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, PR China; Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, PR China
| | - Jun-Guo Lu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, PR China; Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, PR China.
| | - Xiao-Chuang Jin
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, PR China; Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, PR China
| | - Xing-Yu Yang
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, PR China; Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, PR China
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Yang J, Chen G, Zhu S, Wen S, Hu J. Fixed/prescribed-time synchronization of BAM memristive neural networks with time-varying delays via convex analysis. Neural Netw 2023; 163:53-63. [PMID: 37028154 DOI: 10.1016/j.neunet.2023.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 02/26/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023]
Abstract
The synchronization problem of bidirectional associative memory memristive neural networks (BAMMNNs) with time-varying delays plays an essential role in the implementation and application of neural networks. Firstly, under the framework of the Filippov's solution, the discontinuous parameters of the state-dependent switching are transformed by convex analysis method, which is different from most previous approaches. Secondly, based on Lyapunov function and some inequality techniques, several conditions for the fixed-time synchronization (FXTS) of the drive-response systems are obtained by designing special control strategies. Moreover, the settling time (ST) is estimated by the improved fixed-time stability lemma. Thirdly, the driven-response BAMMNNs are investigated to be synchronized within a prescribed time by designing new controllers based on the FXTS results, where ST is irrelevant to the initial values of BAMMNNs and the parameters of controllers. Finally, a numerical simulation is exhibited to verify the correctness of the conclusions.
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Affiliation(s)
- Jinrong Yang
- College of Science, Wuhan University of Science and Technology, Wuhan 430065, China; Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430065, China.
| | - Guici Chen
- College of Science, Wuhan University of Science and Technology, Wuhan 430065, China; Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430065, China.
| | - Song Zhu
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Shiping Wen
- Centre for Artificial Intelligence, University of Technology Sydney, Sydney, 2007, Australia.
| | - Junhao Hu
- School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China.
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Xiao J, Zhong S, Wen S. Unified Analysis on the Global Dissipativity and Stability of Fractional-Order Multidimension-Valued Memristive Neural Networks With Time Delay. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5656-5665. [PMID: 33950847 DOI: 10.1109/tnnls.2021.3071183] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The unified criteria are analyzed on the global dissipativity and stability for the delayed fractional-order systems of multidimension-valued memristive neural networks (FSMVMNNs) in this article. First, based on the comprehensive knowledge about multidimensional algebra, fractional derivatives, and nonsmooth analysis, we establish the unified model for the studied FSMVMNNs in order to propose a more uniform method to analyze the dynamic behaviors of multidimensional neural networks. Then, by mainly applying the Lyapunov method, employing several new lemmas, and solving some mathematical difficulties, without any separation, we acquire the unified and concise criteria. The derived criteria have many advantages in a smaller calculation, lower conservatism, more diversity, and higher flexibility. Finally, we provide two numerical examples to express the availability and improvements of the theoretical results.
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Extended analysis on the global Mittag-Leffler synchronization problem for fractional-order octonion-valued BAM neural networks. Neural Netw 2022; 154:491-507. [DOI: 10.1016/j.neunet.2022.07.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/16/2022] [Accepted: 07/26/2022] [Indexed: 11/22/2022]
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Synchronization of Epidemic Systems with Neumann Boundary Value under Delayed Impulse. MATHEMATICS 2022. [DOI: 10.3390/math10122064] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
This paper reports the construction of synchronization criteria for the delayed impulsive epidemic models with reaction–diffusion under the Neumann boundary value. Different from the previous literature, the reaction–diffusion epidemic model with a delayed impulse brings mathematical difficulties to this paper. In fact, due to the existence of second-order partial derivatives in the reaction–diffusion model with a delayed impulse, the methods of first-order ordinary differential equations from the previous literature cannot be effectively applied in this paper. However, with the help of the variational method and an appropriate boundedness assumption, a new synchronization criterion is derived, and its effectiveness is illustrated by numerical examples.
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Liu A, Zhao H, Wang Q, Niu S, Gao X, Chen C, Li L. A new predefined-time stability theorem and its application in the synchronization of memristive complex-valued BAM neural networks. Neural Netw 2022; 153:152-163. [PMID: 35724477 DOI: 10.1016/j.neunet.2022.05.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/17/2022]
Abstract
In this paper, two novel and general predefined-time stability lemmas are given and applied to the predefined-time synchronization problem of memristive complex-valued bidirectional associative memory neural networks (MCVBAMNNs). Firstly, different from the generally fixed-time stability lemma, the setting of an adjustable time parameter in the derived predefined-time stability lemma causes it to be more flexible and more general. Secondly, the model studied in the complex-valued BAM neural networks model, which is different from the previous discussion of the real part and imaginary part respectively. It is more practical to study the complex-valued nonseparation. Thirdly, two effective controllers are designed to realize the synchronization performance of BAM neural networks based on the predefined-time stability, and the analysis is given based on general predefined-time synchronization. Finally, the correctness of the theoretical derivation is verified by numerical simulation. A secure communication scheme based on predefined-time synchronization of MCVBAMNNs is proposed, and the effectiveness and superiority of the results are proved.
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Affiliation(s)
- Aidi Liu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Hui Zhao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China.
| | - Qingjie Wang
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Sijie Niu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Xizhan Gao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Chuan Chen
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Lixiang Li
- Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Mittag–Leffler Synchronization of Caputo-Delayed Quaternion BAM Neural Networks via Adaptive and Linear Feedback Control Designs. ELECTRONICS 2022. [DOI: 10.3390/electronics11111746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
The Mittag–Leffler synchronization (MLS) issue for Caputo-delayed quaternion bidirectional associative memory neural networks (BAM-NNs) is studied in this paper. Firstly, a novel lemma is proved by the Laplace transform and inverse transform. Then, without decomposing a quaternion system into subsystems, the adaptive controller and the linear controller are designed to realize MLS. According to the proposed lemma, constructing two different Lyapunov functionals and applying the fractional Razumikhin theorem and inequality techniques, the sufficient criteria of MLS on fractional delayed quaternion BAM-NNs are derived. Finally, two numerical examples are given to illustrate the validity and practicability.
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Finite-Time Synchronization Analysis for BAM Neural Networks with Time-Varying Delays by Applying the Maximum-Value Approach with New Inequalities. MATHEMATICS 2022. [DOI: 10.3390/math10050835] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In this paper, we consider the finite-time synchronization for drive-response BAM neural networks with time-varying delays. Instead of using the finite-time stability theorem and integral inequality method, by using the maximum-value method, two new criteria are obtained to ensure the finite-time synchronization for the considered drive-response systems. The inequalities in our paper, applied to obtaining the maximum-valued and designing the novel controllers, are different from those in existing papers.
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Yan H, Qiao Y, Duan L, Miao J. New inequalities to finite-time synchronization analysis of delayed fractional-order quaternion-valued neural networks. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06976-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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The Boundedness and the Global Mittag-Leffler Synchronization of Fractional-Order Inertial Cohen–Grossberg Neural Networks with Time Delays. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10648-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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