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Wu KN, Ren MZ, Liu XZ. Exponential input-to-state stability of stochastic delay reaction–diffusion neural networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.118] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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2
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Reachable set bounding for neural networks with mixed delays: Reciprocally convex approach. Neural Netw 2020; 125:165-173. [PMID: 32097831 DOI: 10.1016/j.neunet.2020.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 12/24/2019] [Accepted: 02/10/2020] [Indexed: 11/22/2022]
Abstract
This paper discusses the reachable set estimation problem of neural networks with mixed delays. Firstly, by means of the maximal Lyapunov-Krasovskii functional, we obtain a non-ellipsoid form of the reachable set. Further more, when calculating the derivative of the maximum Lyapunov functional, the lower bound lemma and reciprocally convex approach method are used to solve the reciprocally convex combination term, which reduce the related decision variables. Secondly, we extend the results to polytopic uncertainties neural networks and consider the case of uncertain differentiable parameters. Finally, two numerical examples and one application example are listed to show the validity of our methods.
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3
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Li Y, Wang C. Anti-periodic solutions for quaternion-valued inertial neural networks with time-varying delays1. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-182731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yongkun Li
- Department of Mathematics, Yunnan University, Kunming, Yunnan, People’s Republic of China
| | - Chun Wang
- Department of Mathematics, Yunnan University, Kunming, Yunnan, People’s Republic of China
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4
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Delayed impulsive control for exponential synchronization of stochastic reaction–diffusion neural networks with time-varying delays using general integral inequalities. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04223-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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5
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Auxiliary function-based integral inequality approach to robust passivity analysis of neural networks with interval time-varying delay. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.04.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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6
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Van Hien L, Son DT, Trinh H. On Global Dissipativity of Nonautonomous Neural Networks With Multiple Proportional Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:225-231. [PMID: 27775543 DOI: 10.1109/tnnls.2016.2614998] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This brief addresses the problem of global dissipativity analysis of nonautonomous neural networks with multiple proportional delays. By using a novel constructive approach based on some comparison techniques for differential inequalities, new explicit delay-independent conditions are derived using M-matrix theory to ensure the existence of generalized exponential attracting sets and the global dissipativity of the system. The method presented in this brief is also utilized to derive a generalized exponential estimate for a class of Halanay-type inequalities with proportional delays. Finally, three numerical examples are given to illustrate the effectiveness and improvement of the obtained results.
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7
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Zhou L, Zhu Q, Wang Z, Zhou W, Su H. Adaptive Exponential Synchronization of Multislave Time-Delayed Recurrent Neural Networks With Lévy Noise and Regime Switching. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017; 28:2885-2898. [PMID: 28114083 DOI: 10.1109/tnnls.2016.2609439] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper discusses the problem of adaptive exponential synchronization in mean square for a new neural network model with the following features: 1) the noise is characterized by the Lévy process and the parameters of the model change in line with the Markovian process; 2) the master system is also disturbed by the same Lévy noise; and 3) there are multiple slave systems, and the state matrix of each slave system is an affine function of the state matrices of all slave systems. Based on the Lyapunov functional theory, the generalized Itô's formula, -matrix method, and the adaptive control technique, some criteria are established to ensure the adaptive exponential synchronization in the mean square of the master system and each slave system. Moreover, the update law of the control gain and the dynamic variation of the parameters of the slave systems are provided. Finally, the effectiveness of the synchronization criteria proposed in this paper is verified by a practical example.
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8
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Decomposition approach to the stability of recurrent neural networks with asynchronous time delays in quaternion field. Neural Netw 2017; 94:55-66. [DOI: 10.1016/j.neunet.2017.06.014] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2017] [Revised: 05/26/2017] [Accepted: 06/26/2017] [Indexed: 11/23/2022]
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9
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Dharani S, Rakkiyappan R, Cao J, Alsaedi A. Synchronization of generalized reaction-diffusion neural networks with time-varying delays based on general integral inequalities and sampled-data control approach. Cogn Neurodyn 2017; 11:369-381. [PMID: 28761556 DOI: 10.1007/s11571-017-9438-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 03/21/2017] [Accepted: 04/10/2017] [Indexed: 11/29/2022] Open
Abstract
This paper explores the problem of synchronization of a class of generalized reaction-diffusion neural networks with mixed time-varying delays. The mixed time-varying delays under consideration comprise of both discrete and distributed delays. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Using a newly introduced integral inequality, less conservative synchronization criteria that assure the global asymptotic synchronization of the considered generalized reaction-diffusion neural network and mixed delays are established in terms of linear matrix inequalities (LMIs). The obtained easy-to-test LMI-based synchronization criteria depends on the delay bounds in addition to the reaction-diffusion terms, which is more practicable. Upon solving these LMIs by using Matlab LMI control toolbox, a desired sampled-data controller gain can be acuqired without any difficulty. Finally, numerical examples are exploited to express the validity of the derived LMI-based synchronization criteria.
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Affiliation(s)
- S Dharani
- School of Mathematics, Bharathiar University, Coimbatore, Tamilnadu 641 046 India
| | - R Rakkiyappan
- School of Mathematics, Bharathiar University, Coimbatore, Tamilnadu 641 046 India
| | - Jinde Cao
- School of Mathematics and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096 China.,Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| | - Ahmed Alsaedi
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
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10
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New Results on Reachable Sets Bounding for Switched Neural Networks Systems with Discrete, Distributed Delays and Bounded Disturbances. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9596-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Dharani S, Rakkiyappan R, Park JH. Pinning sampled-data synchronization of coupled inertial neural networks with reaction-diffusion terms and time-varying delays. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.098] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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12
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Zhou L, Liu X. Mean-square exponential input-to-state stability of stochastic recurrent neural networks with multi-proportional delays. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.038] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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13
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Shi K, Liu X, Tang Y, Zhu H, Zhong S. Some novel approaches on state estimation of delayed neural networks. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.08.064] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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14
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Song Y, Sun W, Jiang F. Mean-square exponential input-to-state stability for neutral stochastic neural networks with mixed delays. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.03.048] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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15
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Muthukumar P, Subramanian K. Stability criteria for Markovian jump neural networks with mode-dependent additive time-varying delays via quadratic convex combination. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.03.058] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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16
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A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.12.040] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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17
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Thuan M, Trinh H, Hien L. New inequality-based approach to passivity analysis of neural networks with interval time-varying delay. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.02.051] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Xie W, Zhu Q, Jiang F. Exponential stability of stochastic neural networks with leakage delays and expectations in the coefficients. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.08.086] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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19
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Rakkiyappan R, Dharani S, Cao J. Synchronization of Neural Networks With Control Packet Loss and Time-Varying Delay via Stochastic Sampled-Data Controller. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2015; 26:3215-3226. [PMID: 25966486 DOI: 10.1109/tnnls.2015.2425881] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This paper addresses the problem of exponential synchronization of neural networks with time-varying delays. A sampled-data controller with stochastically varying sampling intervals is considered. The novelty of this paper lies in the fact that the control packet loss from the controller to the actuator is considered, which may occur in many real-world situations. Sufficient conditions for the exponential synchronization in the mean square sense are derived in terms of linear matrix inequalities (LMIs) by constructing a proper Lyapunov-Krasovskii functional that involves more information about the delay bounds and by employing some inequality techniques. Moreover, the obtained LMIs can be easily checked for their feasibility through any of the available MATLAB tool boxes. Numerical examples are provided to validate the theoretical results.
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20
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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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21
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Sampled-data synchronization of randomly coupled reaction–diffusion neural networks with Markovian jumping and mixed delays using multiple integral approach. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2079-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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Mean square exponential stability of stochastic fuzzy delayed Cohen–Grossberg neural networks with expectations in the coefficients. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.04.020] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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23
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Li JN, Li LS. Mean-square exponential stability for stochastic discrete-time recurrent neural networks with mixed time delays. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.10.020] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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24
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New delay-dependent stability criteria for switched Hopfield neural networks of neutral type with additive time-varying delay components. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.10.014] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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25
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Feng G, Cao J. Single controller for stability of delayed neural networks with mixed coupling and L2-gain condition. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.07.042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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26
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Bhattacharyya S, Pal P, Bhowmick S. Binary image denoising using a quantum multilayer self organizing neural network. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.08.027] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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27
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28
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Less conservative stability criteria for stochastic discrete-time recurrent neural networks with the time-varying delay. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.12.031] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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29
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Zhang H, Gong D, Chen B, Liu Z. Synchronization for coupled neural networks with interval delay: a novel augmented Lyapunov-Krasovskii functional method. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2013; 24:58-70. [PMID: 24808207 DOI: 10.1109/tnnls.2012.2225444] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This paper is concerned with the synchronization problems for an array of neural networks with hybrid coupling and interval time-varying delay. First, a novel augmented Lyapunov-Krasovskii functional (LKF) method is proposed to develop delay-dependent synchronization criteria for the networks, which makes use of more relaxed conditions by employing the new type of augmented matrices with Kronecker product operation. The proposed method can handle a multitude of Kronecker product operations in the LKF and alleviates the requirements of the positive definiteness of some conditional matrices which are usually considered in the existing methods for complex networks. This leads to a significant improvement in the performance of the synchronization criteria, i.e., less conservative synchronization results can be obtained. Meanwhile, the case of fast time-varying delay can also be handled by the proposed method. Furthermore, based on the derived criteria, a robust synchronization criterion is obtained for the system with uncertainties both in coefficient and coupling matrix terms. Since an expression based on linear matrix inequality is used, the proposed criteria can be easily checked in practice. Finally, numerical examples are provided to show the effectiveness of the proposed method.
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30
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Huang T, Li C, Duan S, Starzyk JA. Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2012; 23:866-875. [PMID: 24806759 DOI: 10.1109/tnnls.2012.2192135] [Citation(s) in RCA: 89] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.
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31
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Li X, Gao H, Yu X. A unified approach to the stability of generalized static neural networks with linear fractional uncertainties and delays. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. PART B, CYBERNETICS : A PUBLICATION OF THE IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETY 2011; 41:1275-86. [PMID: 21926000 DOI: 10.1109/tsmcb.2011.2125950] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, the robust global asymptotic stability (RGAS) of generalized static neural networks (SNNs) with linear fractional uncertainties and a constant or time-varying delay is concerned within a novel input-output framework. The activation functions in the model are assumed to satisfy a more general condition than the usually used Lipschitz-type ones. First, by four steps of technical transformations, the original generalized SNN model is equivalently converted into the interconnection of two subsystems, where the forward one is a linear time-invariant system with a constant delay while the feedback one bears the norm-bounded property. Then, based on the scaled small gain theorem, delay-dependent sufficient conditions for the RGAS of generalized SNNs are derived via combining a complete Lyapunov functional and the celebrated discretization scheme. All the results are given in terms of linear matrix inequalities so that the RGAS problem of generalized SNNs is projected into the feasibility of convex optimization problems that can be readily solved by effective numerical algorithms. The effectiveness and superiority of our results over the existing ones are demonstrated by two numerical examples.
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Affiliation(s)
- Xianwei Li
- Research Institute of Intelligent Control and Systems, Harbin Institute of Technology (HIT), Harbin, China.
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32
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Liu G, Yang SX, Chai Y, Feng W, Fu W. Robust stability criteria for uncertain stochastic neural networks of neutral-type with interval time-varying delays. Neural Comput Appl 2011. [DOI: 10.1007/s00521-011-0696-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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33
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Deng F, Hua M, Liu X, Peng Y, Fei J. Robust delay-dependent exponential stability for uncertain stochastic neural networks with mixed delays. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2010.08.027] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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34
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Quanxin Zhu, Jinde Cao. Exponential Stability of Stochastic Neural Networks With Both Markovian Jump Parameters and Mixed Time Delays. ACTA ACUST UNITED AC 2011; 41:341-53. [DOI: 10.1109/tsmcb.2010.2053354] [Citation(s) in RCA: 191] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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35
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36
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Hua M, Liu X, Deng F, Fei J. New Results on Robust Exponential Stability of Uncertain Stochastic Neural Networks with Mixed Time-Varying Delays. Neural Process Lett 2010. [DOI: 10.1007/s11063-010-9152-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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37
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Liu X. Synchronization of linearly coupled neural networks with reaction–diffusion terms and unbounded time delays. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2010.05.003] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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38
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Huaguang Zhang, Zhenwei Liu, Guang-Bin Huang, Zhanshan Wang. Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With Time-Varying Delay. ACTA ACUST UNITED AC 2010; 21:91-106. [DOI: 10.1109/tnn.2009.2034742] [Citation(s) in RCA: 355] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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39
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Delay-distribution-dependent stability of stochastic discrete-time neural networks with randomly mixed time-varying delays. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2009.05.012] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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40
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Zhou W, Lu H, Duan C. Exponential stability of hybrid stochastic neural networks with mixed time delays and nonlinearity. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2009.04.012] [Citation(s) in RCA: 10] [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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41
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Rongni Yang, Huijun Gao, Peng Shi. Novel Robust Stability Criteria for Stochastic Hopfield Neural Networks With Time Delays. ACTA ACUST UNITED AC 2009; 39:467-74. [DOI: 10.1109/tsmcb.2008.2006860] [Citation(s) in RCA: 147] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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42
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Dong Yue, Yijun Zhang, Engang Tian, Chen Peng. Delay-Distribution-Dependent Exponential Stability Criteria for Discrete-Time Recurrent Neural Networks With Stochastic Delay. ACTA ACUST UNITED AC 2008. [DOI: 10.1109/tnn.2008.2000166] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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43
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Lu CY, Tsai HH, Su TJ, Tsai JSH, Liao CW. A Delay-Dependent Approach to Passivity Analysis for Uncertain Neural Networks with Time-varying Delay. Neural Process Lett 2008. [DOI: 10.1007/s11063-008-9072-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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44
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Liu Y, Wang Z, Liu X. On global exponential stability of generalized stochastic neural networks with mixed time-delays. Neurocomputing 2006. [DOI: 10.1016/j.neucom.2006.01.031] [Citation(s) in RCA: 81] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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45
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He Huang, Ho D, Lam J. Stochastic stability analysis of fuzzy hopfield neural networks with time-varying delays. ACTA ACUST UNITED AC 2005. [DOI: 10.1109/tcsii.2005.846305] [Citation(s) in RCA: 217] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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46
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Towghi N, Javidi B. Image recognition in the presence of non-Gaussian noise with unknown statistics. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2001; 18:2744-2753. [PMID: 11688864 DOI: 10.1364/josaa.18.002744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
We design receivers to detect a known pattern or a reference signal in the presence of very general and non-Gaussian types of noise. Three sources of input-noise degradation are considered: additive, multiplicative, and disjoint background. The detection process involves two steps: (1) estimation of the relevant noise parameters within the framework of hypothesis testing and (2) maximizing a certain metric that measures the likelihood of the target being at a given location. The parameter estimation portion is carried out by moment-matching techniques. Because of the number of unknown parameters and the fact that various types of input-noise processes are non-Gaussian, the methods that are used to estimate these parameters differ from the standard methods of maximizing the likelihood function. To verify the existence of the target at a certain location, we use l(p)-norm metric for p > or = 0 to measure the likelihood of the target being present at the location of interest. Computer simulations are used to show that for the images tested here, the receivers designed herein perform better than some existing receivers.
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Affiliation(s)
- N Towghi
- Department Electrical and Systems Engineering, University of Connecticut, Storrs 06269-2157, USA
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47
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Wen-Jing Li, Lee T. Hopfield neural networks for affine invariant matching. ACTA ACUST UNITED AC 2001; 12:1400-10. [DOI: 10.1109/72.963776] [Citation(s) in RCA: 71] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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48
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Young SS, Scott PD, Bandera C. Foveal automatic target recognition using a multiresolution neural network. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 1998; 7:1122-1135. [PMID: 18276329 DOI: 10.1109/83.704306] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
This paper presents a method for detecting and classifying a target from its foveal (graded resolution) imagery using a multiresolution neural network. Target identification decisions are based on minimizing an energy function. This energy function is evaluated by comparing a candidate blob with a library of target models at several levels of resolution simultaneously available in the current foveal image. For this purpose, a concurrent (top-down-and-bottom-up) matching procedure is implemented via a novel multilayer Hopfield neural network. The associated energy function supports not only interactions between cells at the same resolution level, but also between sets of nodes at distinct resolution levels. This permits features at different resolution levels to corroborate or refute one another contributing to an efficient evaluation of potential matches. Gaze control, refoveation to more salient regions of the available image space, is implemented as a search for high resolution features which will disambiguate the candidate blob. Tests using real two-dimensional (2-D) objects and their simulated foveal imagery are provided.
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Affiliation(s)
- S S Young
- Health Imaging Res. Imaging Res. Lab., Eastman Kodak Co., Rochester, NY 14650-2033, USA.
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