1
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Li J, Gu C, Wu Z. Online distributed stochastic learning algorithm for convex optimization in time-varying directed networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.03.094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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2
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Yang X, Li C, Song Q, Li H, Huang J. Effects of State-Dependent Impulses on Robust Exponential Stability of Quaternion-Valued Neural Networks Under Parametric Uncertainty. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:2197-2211. [PMID: 30507516 DOI: 10.1109/tnnls.2018.2877152] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper addresses the state-dependent impulsive effects on robust exponential stability of quaternion-valued neural networks (QVNNs) with parametric uncertainties. In view of the noncommutativity of quaternion multiplication, we have to separate the concerned quaternion-valued models into four real-valued parts. Then, several assumptions ensuring every solution of the separated state-dependent impulsive neural networks intersects each of the discontinuous surface exactly once are proposed. In the meantime, by applying the B -equivalent method, the addressed state-dependent impulsive models are reduced to fixed-time ones, and the latter can be regarded as the comparative systems of the former. For the subsequent analysis, we proposed a novel norm inequality of block matrix, which can be utilized to analyze the same stability properties of the separated state-dependent impulsive models and the reduced ones efficaciously. Afterward, several sufficient conditions are well presented to guarantee the robust exponential stability of the origin of the considered models; it is worth mentioning that two cases of addressed models are analyzed concretely, that is, models with exponential stable continuous subsystems and destabilizing impulses, and models with unstable continuous subsystems and stabilizing impulses. In addition, an application case corresponding to the stability problem of models with unstable continuous subsystems and stabilizing impulses for state-dependent impulse control to robust exponential synchronization of QVNNs is considered summarily. Finally, some numerical examples are proffered to illustrate the effectiveness and correctness of the obtained results.
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Wang H, Tan J, Huang T, Duan S. Impulsive delayed integro-differential inequality and its application on IMNNs with discrete and distributed delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.03.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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4
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Exponential stability criterion of high-order BAM neural networks with delays and impulse via fixed point approach. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.02.081] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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5
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Zhao Y, He X, Huang T, Huang J. Smoothing inertial projection neural network for minimization Lp−q in sparse signal reconstruction. Neural Netw 2018; 99:31-41. [DOI: 10.1016/j.neunet.2017.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 10/21/2017] [Accepted: 12/12/2017] [Indexed: 10/18/2022]
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6
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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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7
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Zhou Y, Li C, Chen L, Huang T. Global exponential stability of memristive Cohen–Grossberg neural networks with mixed delays and impulse time window. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.11.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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8
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Zhang W, Huang T, He X, Li C. Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses. Neural Netw 2017; 95:102-109. [DOI: 10.1016/j.neunet.2017.03.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 02/28/2017] [Accepted: 03/28/2017] [Indexed: 11/30/2022]
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9
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Zhang X, Li C, Huang T, Ahmad HG. Effects of variable-time impulses on global exponential stability of Cohen–Grossberg neural networks. INT J BIOMATH 2017. [DOI: 10.1142/s1793524517501170] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We investigate the global exponential stability of Cohen–Grossberg neural networks (CGNNs) with variable moments of impulses using B-equivalence method. Under certain conditions, we show that each solution of the considered system intersects each surface of discontinuity exactly once, and that the variable-time impulsive systems can be reduced to the fixed-time impulsive ones. The obtained results imply that impulsive CGNN will remain stability property of continuous subsystem even if the impulses are of somewhat destabilizing, and that stabilizing impulses can stabilize the unstable continuous subsystem at its equilibrium points. Moreover, two stability criteria for the considered CGNN by use of proposed comparison system are obtained. Finally, the theoretical results are illustrated by two examples.
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Affiliation(s)
- Xianxiu Zhang
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, P. R. China
- Department of Mathematics, Liupanshui Normal University, Guizhou, Liupanshui 553001, P. R. China
| | - Chuandong Li
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, P. R. China
| | - Tingwen Huang
- Department of Mathematics, Texas A&M University at Qatar, Doha 23874, Qatar
| | - Hafiz Gulfam Ahmad
- Department of Computer Science and IT, Ghazi University, D. G. Khan 32260, Pakistan
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10
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Zhang X, Li C, Huang T. Hybrid impulsive and switching Hopfield neural networks with state-dependent impulses. Neural Netw 2017. [PMID: 28646762 DOI: 10.1016/j.neunet.2017.04.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
We discuss the global stability of switching Hopfield neural networks (HNN) with state-dependent impulses using B-equivalence method. Under certain conditions, we show that the state-dependent impulsive switching systems can be reduced to the fixed-time ones, and that the global stability of corresponding comparison system implies the same stability of the considered system. On this basis, a novel stability criterion for the considered HNN is established. Finally, two numerical examples are given to demonstrate the effectiveness of our results.
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Affiliation(s)
- Xianxiu Zhang
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; Department of Mathematics, Liupanshui Normal University, Guizhou Liupanshui 553001, China.
| | - Chuandong Li
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Tingwen Huang
- Department of Mathematics, Texas A&M University at Qatar, Doha, Qatar
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11
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Stability Analysis of TS Fuzzy System with State-Dependent Impulses. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9657-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Novel Existence and Stability Criteria of Periodic Solutions for Impulsive Delayed Neural Networks Via Coefficient Integral Averages. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.08.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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13
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Wang X, Wang H, Li C, Huang T. Stability analysis of hybrid neural networks with impulsive time window. INT J BIOMATH 2016. [DOI: 10.1142/s1793524517500115] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The urgent problem with impulsive moments cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, in this paper, a novel class of system with impulsive time window is proposed. Different from the conventional impulsive control strategies, the main characteristic of the impulsive time window is that impulse occurs in a random manner. Moreover, for the importance of the hybrid neural networks, using switching Lyapunov functions and a generalized Hanlanay inequality, some general criteria for asymptotic and exponential stability of the hybrid neural networks with impulsive time window are established. Finally, some simulations are provided to further illustrate the effectiveness of the results.
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Affiliation(s)
- Xin Wang
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronics and Information Engineering, Southwest University, Chongqing 400715, P. R. China
- Key Laboratory of Machine Perception and Children’s Intelligence Development, Chongqing University of Education, P. R. China
| | - Hui Wang
- College of Mathematics Science, Chongqing Normal University, Chongqing 401331, P. R. China
| | - Chuandong Li
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronics and Information Engineering, Southwest University, Chongqing 400715, P. R. China
| | - Tingwen Huang
- Texas A&M University at Qatar, Doha, P. O. Box 23874, Qatar
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14
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Yang X, Li C, Song Q, Huang T, Chen X. Mittag–Leffler stability analysis on variable-time impulsive fractional-order neural networks. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.04.045] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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15
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Finite-time stabilization of uncertain neural networks with distributed time-varying delays. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2421-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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16
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17
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Yang S, Li C, Huang T. Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control. Neural Netw 2016; 75:162-72. [DOI: 10.1016/j.neunet.2015.12.003] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 12/07/2015] [Accepted: 12/08/2015] [Indexed: 11/27/2022]
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18
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19
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Liu C, Liu W, Yang Z, Liu X, Li C, Zhang G. Stability of neural networks with delay and variable-time impulses. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.07.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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20
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Senthilraj S, Raja R, Jiang F, Zhu Q, Samidurai R. New delay-interval-dependent stability analysis of neutral type BAM neural networks with successive time delay components. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.07.060] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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21
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Zhou Y, Li C, Huang T, Wang X. Impulsive stabilization and synchronization of Hopfield-type neural networks with impulse time window. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2105-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Wang H, Duan S, Li C, Wang L, Huang T. Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2094-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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23
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Song Q, Zhao Z, Liu Y. Impulsive effects on stability of discrete-time complex-valued neural networks with both discrete and distributed time-varying delays. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.05.020] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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24
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Wang F, Yang Y, Xu X, Li L. Global asymptotic stability of impulsive fractional-order BAM neural networks with time delay. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2063-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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25
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Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control. Neural Netw 2015; 71:105-11. [PMID: 26319051 DOI: 10.1016/j.neunet.2015.08.002] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Revised: 06/01/2015] [Accepted: 08/06/2015] [Indexed: 11/24/2022]
Abstract
In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper.
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26
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Zhou L. Novel global exponential stability criteria for hybrid BAM neural networks with proportional delays. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.02.061] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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27
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Qi J, Li C, Huang T. Stability of inertial BAM neural network with time-varying delay via impulsive control. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.02.052] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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28
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Qi J, Li C, Huang T, Zhang W. Exponential Stability of Switched Time-varying Delayed Neural Networks with All Modes Being Unstable. Neural Process Lett 2015. [DOI: 10.1007/s11063-015-9428-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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29
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Wang F, Sun D, Wu H. Global exponential stability and periodic solutions of high-order bidirectional associative memory (BAM) neural networks with time delays and impulses. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.12.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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30
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Tan M, Xu S, Li Z. Dynamics of High-order Fuzzy Cellular Neural Networks with Time-varying Delays. INT J COMPUT INT SYS 2015. [DOI: 10.1080/18756891.2015.1017368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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31
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Wei L, Chen WH. Global exponential stability of a class of impulsive neural networks with unstable continuous and discrete dynamics. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.06.072] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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32
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Şaylı M, Yılmaz E. Global robust asymptotic stability of variable-time impulsive BAM neural networks. Neural Netw 2014; 60:67-73. [DOI: 10.1016/j.neunet.2014.07.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 07/31/2014] [Indexed: 10/24/2022]
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33
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Delay-dependent robust stability and stabilization of uncertain memristive delay neural networks. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.03.027] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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34
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Synchronization control for stochastic neural networks with mixed time-varying delays. ScientificWorldJournal 2014; 2014:840185. [PMID: 25110747 PMCID: PMC4106077 DOI: 10.1155/2014/840185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2014] [Accepted: 06/07/2014] [Indexed: 11/18/2022] Open
Abstract
Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results.
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35
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Exponential stability of impulsive discrete-time stochastic BAM neural networks with time-varying delay. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.10.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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36
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Chen L, Li C, Huang T, Wang X. Quick noise-tolerant learning in a multi-layer memristive neural network. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.05.043] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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37
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Zhang A, Qiu J, She J. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks. Neural Netw 2014; 50:98-109. [DOI: 10.1016/j.neunet.2013.11.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 09/28/2013] [Accepted: 11/10/2013] [Indexed: 10/26/2022]
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38
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Impulsive control for synchronizing delayed discrete complex networks with switching topology. Neural Comput Appl 2014; 24:59-68. [PMID: 24415851 PMCID: PMC3882576 DOI: 10.1007/s00521-013-1470-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 07/29/2013] [Indexed: 11/24/2022]
Abstract
In this paper, global exponential synchronization of a class of discrete delayed complex networks with switching topology has been investigated by using Lyapunov-Ruzimiki method. The impulsive scheme is designed to work at the time instant of switching occurrence. A time-varying delay-dependent criterion for impulsive synchronization is given to ensure the delayed discrete complex networks switching topology tending to a synchronous state. Furthermore, a numerical simulation is given to illustrate the effectiveness of main results
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39
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Li J, Li C, Wu Z, Huang J. A feedback neural network for solving convex quadratic bi-level programming problems. Neural Comput Appl 2013. [DOI: 10.1007/s00521-013-1530-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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40
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Wu H, Liao X, Feng W, Guo S. Mean square stability of uncertain stochastic BAM neural networks with interval time-varying delays. Cogn Neurodyn 2013; 6:443-58. [PMID: 24082964 DOI: 10.1007/s11571-012-9200-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2011] [Revised: 02/10/2012] [Accepted: 03/25/2012] [Indexed: 11/27/2022] Open
Abstract
The robust asymptotic stability analysis for uncertain BAM neural networks with both interval time-varying delays and stochastic disturbances is considered. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which takes into account the ranges for delays, some new stability criteria are established to guarantee the delayed BAM neural networks to be robustly asymptotically stable in the mean square. Unlike the most existing mean square stability conditions for BAM neural networks, the supplementary requirements that the time derivatives of time-varying delays must be smaller than 1 are released and the lower bounds of time varying delays are not restricted to be 0. Furthermore, in the proposed scheme, the stability conditions are delay-range-dependent and rate-dependent/independent. As a result, the new criteria are applicable to both fast and slow time-varying delays. Three numerical examples are given to illustrate the effectiveness of the proposed criteria.
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Affiliation(s)
- Haixia Wu
- College of Computer Science, Chongqing University, Chongqing, 400030 People's Republic of China ; Department of Computer Science, Chongqing Education College, Chongqing, 400067 People's Republic of China
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41
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42
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Analysis on equilibrium points of cellular neural networks with thresholding activation function. Neural Comput Appl 2013. [DOI: 10.1007/s00521-012-1173-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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43
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Global exponential stability of a class of memristive neural networks with time-varying delays. Neural Comput Appl 2013. [DOI: 10.1007/s00521-013-1383-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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44
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45
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Wen S, Zeng Z, Huang T. Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses. Neural Process Lett 2012. [DOI: 10.1007/s11063-012-9263-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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46
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Exponential stability of stochastic high-order BAM neural networks with time delays and impulsive effects. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-0861-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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47
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