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Singh S, Kumar U, Das S, Cao J. Global Exponential Stability of Inertial Cohen–Grossberg Neural Networks with Time-Varying Delays via Feedback and Adaptive Control Schemes: Non-reduction Order Approach. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11044-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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2
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Wu K, Jian J. Global Robust Exponential Dissipativity of Uncertain Second-Order BAM Neural Networks With Mixed Time-Varying Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:5675-5687. [PMID: 33079675 DOI: 10.1109/tnnls.2020.3027326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article focuses on the global robust exponential dissipativity (GRED) of uncertain second-order BAM neural networks with mixed time-varying delays. First, a new differential inequality for the concerned second-order system is established. Second, by constructing some new Lyapunov-Krasovskii functionals (LKFs) and applying this new inequality and some other inequalities, some new GRED criteria in the form of linear matrix inequalities are presented. The global exponential attractive sets are also provided simultaneously. Different from the existing reduced-order methods, this article considers some new LKFs to directly analyze the dynamics of the addressed system via a nonreduced-order strategy. Finally, the correctness of the theoretical results is verified by simulation experiments.
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3
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Wei F, Chen G, Wang W. Finite-time stabilization of memristor-based inertial neural networks with time-varying delays combined with interval matrix method. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107395] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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4
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Zhang T, Jian J. New results on synchronization for second-order fuzzy memristive neural networks with time-varying and infinite distributed delays. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107397] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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5
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Intermittent Control Based Exponential Synchronization of Inertial Neural Networks with Mixed Delays. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10574-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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6
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Wu K, Jian J. Non-reduced order strategies for global dissipativity of memristive neutral-type inertial neural networks with mixed time-varying delays. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.12.120] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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7
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Song X, Man J, Song S, Wang Z. An improved result on synchronization control for memristive neural networks with inertial terms and reaction-diffusion items. ISA TRANSACTIONS 2020; 99:74-83. [PMID: 31699400 DOI: 10.1016/j.isatra.2019.10.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 06/10/2023]
Abstract
This paper investigates the synchronization issue of the memristive neural networks (MNNs) with inertial terms and reaction-diffusion items. In order to smoothly derive the controller gains and obtain an excellent control effect, the desired controller that contains a discontinuous function is proposed. Moreover, by constructing a novel Lyapunov-Krasovskii functional and combining the inequality techniques, several sufficient conditions in terms of algebraic inequalities are obtained to guarantee the synchronization of the proposed drive and response systems. Finally, three numerical simulations are exploited to support the acquired theoretical results.
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Affiliation(s)
- Xiaona Song
- School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
| | - Jingtao Man
- School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
| | - Shuai Song
- School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China.
| | - Zhen Wang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China.
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8
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Duan L, Jian J, Wang B. Global exponential dissipativity of neutral-type BAM inertial neural networks with mixed time-varying delays. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.082] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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Duan L, Jian J. Global Lagrange Stability of Inertial Neutral Type Neural Networks with Mixed Time-Varying Delays. Neural Process Lett 2020. [DOI: 10.1007/s11063-019-10177-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Zhang M, Wang D. Robust dissipativity analysis for delayed memristor-based inertial neural network. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Wan P, Sun D, Chen D, Zhao M, Zheng L. Exponential synchronization of inertial reaction-diffusion coupled neural networks with proportional delay via periodically intermittent control. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.05.028] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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12
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Exponential synchronization of inertial neural networks with mixed time-varying delays via periodically intermittent control. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.01.096] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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13
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Stability property of impulsive inertial neural networks with unbounded time delay and saturating actuators. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04115-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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14
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Exponential stability in Lagrange sense for inertial neural networks with time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.12.063] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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15
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Li Y, Xiang J. Existence and global exponential stability of anti-periodic solution for Clifford-valued inertial Cohen–Grossberg neural networks with delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.12.064] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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16
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Yu T, Liu S, Wang H, Cui Y, Cao D. Robust delay-dependent stability of uncertain inertial neural networks with impulsive effects and distributed-delay. INT J BIOMATH 2019. [DOI: 10.1142/s1793524519500104] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The robust stability problem of uncertain inertial neural networks with impulsive effects and distributed-delay is considered in the present paper. The average impulsive interval and differential inequality for delay differential equations are used to obtain the global exponential stability of the inertial neural networks. The robust distributed-delay-dependent stability criteria here are proposed in terms of both linear matrix inequalities and algebraic inequalities. Our results can not only be used to obtain the stability of the uncertain inertial neural network with impulsive disturbance, but also be utilized to design the impulsive control for the uncertain inertial neural networks. The novel criteria complement and extend the previous works on uncertain inertial neural network with/without impulsive effects. Typical numerical examples are used to test the validity of the developed stability criteria finally.
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Affiliation(s)
- Tianhu Yu
- Department of Mathematics, Luoyang Normal University, Luoyang 471934, P. R. China
| | - Shengqiang Liu
- Department of Mathematics, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Huamin Wang
- Department of Mathematics, Luoyang Normal University, Luoyang 471934, P. R. China
| | - Yingjia Cui
- Department of Mathematics, Luoyang Normal University, Luoyang 471934, P. R. China
| | - Dengqing Cao
- School of Astronautics, Harbin Institute of Technology, P. O. Box 137, Harbin 150001, P. R. China
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Zhang R, Zeng D, Park JH, Liu Y, Zhong S. Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:6385-6395. [PMID: 29994336 DOI: 10.1109/tnnls.2018.2836339] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.
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Yu T, Wang H, Su M, Cao D. Distributed-delay-dependent exponential stability of impulsive neural networks with inertial term. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.06.033] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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19
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Exponential synchronization of complex networks with continuous dynamics and Boolean mechanism. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.061] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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20
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Stability of Inertial Neural Network with Time-Varying Delays Via Sampled-Data Control. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9905-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Van Hien L, Hai-An LD. Positive solutions and exponential stability of positive equilibrium of inertial neural networks with multiple time-varying delays. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3536-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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22
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Global asymptotical stability for a class of non-autonomous impulsive inertial neural networks with unbounded time-varying delay. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3498-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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23
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Wan P, Jian J. Passivity analysis of memristor-based impulsive inertial neural networks with time-varying delays. ISA TRANSACTIONS 2018; 74:88-98. [PMID: 29455890 DOI: 10.1016/j.isatra.2018.02.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 12/18/2017] [Accepted: 02/04/2018] [Indexed: 06/08/2023]
Abstract
This paper focuses on delay-dependent passivity analysis for a class of memristive impulsive inertial neural networks with time-varying delays. By choosing proper variable transformation, the memristive inertial neural networks can be rewritten as first-order differential equations. The memristive model presented here is regarded as a switching system rather than employing the theory of differential inclusion and set-value map. Based on matrix inequality and Lyapunov-Krasovskii functional method, several delay-dependent passivity conditions are obtained to ascertain the passivity of the addressed networks. In addition, the results obtained here contain those on the passivity for the addressed networks without impulse effects as special cases and can also be generalized to other neural networks with more complex pulse interference. Finally, one numerical example is presented to show the validity of the obtained results.
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Affiliation(s)
- Peng Wan
- College of Science, China Three Gorges University, Yichang, Hubei, 443002, China.
| | - Jigui Jian
- College of Science, China Three Gorges University, Yichang, Hubei, 443002, China.
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Global Dissipativity of Inertial Neural Networks with Proportional Delay via New Generalized Halanay Inequalities. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9788-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Tang Q, Jian J. Matrix measure based exponential stabilization for complex-valued inertial neural networks with time-varying delays using impulsive control. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.08.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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