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Wang Q, Zhao H, Liu A, Niu S, Gao X, Zong X, Li L. An Improved Fixed-Time Stability Theorem and its Application to the Synchronization of Stochastic Impulsive Neural Networks. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11268-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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2
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Zhang J, Zhu S, Bao G, Liu X, Wen S. Analysis and Design of Multivalued High-Capacity Associative Memories Based on Delayed Recurrent Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:12989-13000. [PMID: 34347620 DOI: 10.1109/tcyb.2021.3095499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This article aims at analyzing and designing the multivalued high-capacity-associative memories based on recurrent neural networks with both asynchronous and distributed delays. In order to increase storage capacities, multivalued activation functions are introduced into associative memories. The stored patterns are retrieved by external input vectors instead of initial conditions, which can guarantee accurate associative memories by avoiding spurious equilibrium points. Some sufficient conditions are proposed to ensure the existence, uniqueness, and global exponential stability of the equilibrium point of neural networks with mixed delays. For neural networks with n neurons, m -dimensional input vectors, and 2k -valued activation functions, the autoassociative memories have (2k)n storage capacities and heteroassociative memories have min {(2k)n,(2k)m} storage capacities. That is, the storage capacities of designed associative memories in this article are obviously higher than the 2n and min {2n,2m} storage capacities of the conventional ones. Three examples are given to support the theoretical results.
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3
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Zhang X, Li C, Li H, Cao Z. Mean-square stabilization of impulsive neural networks with mixed delays by non-fragile feedback involving random uncertainties. Neural Netw 2022; 154:469-480. [DOI: 10.1016/j.neunet.2022.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/19/2022] [Accepted: 07/07/2022] [Indexed: 10/16/2022]
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4
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Stability Analysis of the Impulsive Projection Neural Network. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10901-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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5
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Aouiti C, Bessifi M. Non-chattering quantized control for synchronization in finite–fixed time of delayed Cohen–Grossberg-type fuzzy neural networks with discontinuous activation. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06253-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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6
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Sheng Y, Lewis FL, Zeng Z, Huang T. Lagrange Stability and Finite-Time Stabilization of Fuzzy Memristive Neural Networks With Hybrid Time-Varying Delays. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:2959-2970. [PMID: 31059467 DOI: 10.1109/tcyb.2019.2912890] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper focuses on Lagrange exponential stability and finite-time stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks with discrete and distributed time-varying delays (DFMNNs). By resorting to theories of differential inclusions and the comparison strategy, an algebraic condition is developed to confirm Lagrange exponential stability of the underlying DFMNNs in Filippov's sense, and the exponentially attractive set is estimated. When external input is not considered, global exponential stability of DFMNNs is derived directly, which includes some existing ones as special cases. Furthermore, finite-time stabilization of the addressed DFMNNs is analyzed by exploiting inequality techniques and the comparison approach via designing a nonlinear state feedback controller. The boundedness assumption of activation functions is removed herein. Finally, two simulations are presented to demonstrate the validness of the outcomes, and an application is performed in pseudorandom number generation.
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7
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Ye D, Shao Y. Quasi-synchronization of heterogeneous nonlinear multi-agent systems subject to DOS attacks with impulsive effects. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.095] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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8
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Meng F, Li K, Zhao Z, Song Q, Liu Y, Alsaadi FE. Periodicity of impulsive Cohen–Grossberg-type fuzzy neural networks with hybrid delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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9
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New stochastic synchronization criteria for fuzzy Markovian hybrid neural networks with random coupling strengths. Neural Comput Appl 2019. [DOI: 10.1007/s00521-017-3043-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Meng F, Li K, Song Q, Liu Y, Alsaadi FE. Periodicity of Cohen–Grossberg-type fuzzy neural networks with impulses and time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.10.038] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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11
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Shan Q, Zhang H, Wang Z, Zhang Z. Global Asymptotic Stability and Stabilization of Neural Networks With General Noise. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:597-607. [PMID: 28055925 DOI: 10.1109/tnnls.2016.2637567] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Neural networks (NNs) in the stochastic environment were widely modeled as stochastic differential equations, which were driven by white noise, such as Brown or Wiener process in the existing papers. However, they are not necessarily the best models to describe dynamic characters of NNs disturbed by nonwhite noise in some specific situations. In this paper, general noise disturbance, which may be nonwhite, is introduced to NNs. Since NNs with nonwhite noise cannot be described by Itô integral equation, a novel modeling method of stochastic NNs is utilized. By a framework in light of random field approach and Lyapunov theory, the global asymptotic stability and stabilization in probability or in the mean square of NNs with general noise are analyzed, respectively. Criteria for the concerned systems based on linear matrix inequality are proposed. Some examples are given to illustrate the effectiveness of the obtained results.
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12
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Stochastic synchronization for an array of hybrid neural networks with random coupling strengths and unbounded distributed delays. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.07.062] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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13
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Chen WH, Luo S, Zheng WX. Generating Globally Stable Periodic Solutions of Delayed Neural Networks With Periodic Coefficients via Impulsive Control. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:1590-1603. [PMID: 30148709 DOI: 10.1109/tcyb.2016.2552383] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper is dedicated to designing periodic impulsive control strategy for generating globally stable periodic solutions for periodic neural networks with discrete and unbounded distributed delays when such neural networks do not have stable periodic solutions. Two criteria for the existence of globally exponentially stable periodic solutions are developed. The first one can deal with the case where no bounds on the derivative of the discrete delay are given, while the second one is a refined version of the first one when the discrete delay is constant. Both stability criteria possess several adjustable parameters, which will increase the flexibility for designing impulsive control laws. In particular, choosing appropriate adjustable parameters can lead to partial state impulsive control laws for certain periodic neural networks. The proof techniques employed includes two aspects. In the first aspect, by choosing a weighted phase space PCα, a sufficient condition for the existence of a unique periodic solution is derived by virtue of the contraction mapping principle. In the second aspect, by choosing an impulse-time-dependent Lyapunov function/functional to capture the dynamical characteristics of the impulsively controlled neural networks, improved stability criteria for periodic solutions are attained. Three numerical examples are given to illustrate the efficiency of the proposed results.
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14
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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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15
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Wang L, Song Q, Liu Y, Zhao Z, Alsaadi FE. Global asymptotic stability of impulsive fractional-order complex-valued neural networks with time delay. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.02.086] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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16
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Zheng CD, Xian Y, Wang Z. Third-order reciprocally convex approach to stability of fuzzy cellular neural networks under impulsive perturbations. Soft comput 2017. [DOI: 10.1007/s00500-016-2051-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Rao R, Zhong S, Pu Z. On the role of diffusion factors in stability analysis for p-Laplace dynamical equations involved to BAM Cohen-Grossberg neural network. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.036] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Zheng CD, Xian Y. On synchronization for chaotic memristor-based neural networks with time-varying delays. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.08.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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19
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Tan J, Li C. Finite-Time Stability of Neural Networks with Impulse Effects and Time-Varying Delay. Neural Process Lett 2016. [DOI: 10.1007/s11063-016-9570-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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20
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Existence and Global Exponential Stability of Periodic Solution for a Class of Neutral-Type Neural Networks with Time Delays. Neural Process Lett 2016. [DOI: 10.1007/s11063-016-9549-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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21
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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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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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Robust stability of stochastic fuzzy delayed neural networks with impulsive time window. Neural Netw 2015; 67:84-91. [PMID: 25897509 DOI: 10.1016/j.neunet.2015.03.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2014] [Revised: 03/16/2015] [Accepted: 03/19/2015] [Indexed: 11/23/2022]
Abstract
The urgent problem of impulsive moments which cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, the novel concept of impulsive time window is proposed in this paper. And the stability problem of stochastic fuzzy uncertain delayed neural networks with impulsive time window is investigated. By combining the discretized Lyapunov function approach with mathematical induction method, several novel and easy-to-check sufficient conditions concerning the impulsive time window are derived to ensure that the model considered here is exponentially stable in mean square. Numerical simulations are presented to further demonstrate the effectiveness of the proposed stability criterion.
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25
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Zhang H, Huang Y, Wang B, Wang Z. Design and analysis of associative memories based on external inputs of delayed recurrent neural networks. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.12.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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26
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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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27
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Yang W. Periodic Solution for Fuzzy Cohen–Grossberg BAM Neural Networks with Both Time-Varying and Distributed Delays and Variable Coefficients. Neural Process Lett 2013. [DOI: 10.1007/s11063-013-9310-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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28
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Jin X, Guan W, Ye D. Robust Adaptive Synchronization Control for a Class of Perturbed and Delayed Neural Networks. Neural Process Lett 2013. [DOI: 10.1007/s11063-013-9300-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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29
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Chandran R, Balasubramaniam P. Delay Dependent Exponential Stability for Fuzzy Recurrent Neural Networks with Interval Time-Varying Delay. Neural Process Lett 2012. [DOI: 10.1007/s11063-012-9239-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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Global exponential stability of impulsive fuzzy Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.02.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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31
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State estimation of recurrent neural networks with interval time-varying delay: an improved delay-dependent approach. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-1061-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Wang C, Kao Y, Yang G. Exponential stability of impulsive stochastic fuzzy reaction–diffusion Cohen–Grossberg neural networks with mixed delays. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.01.022] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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33
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Exponential stability of impulsive discrete systems with time delay and applications in stochastic neural networks: A Razumikhin approach. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.09.029] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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34
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NIU SHUYUN, JIANG HAIJUN, TENG ZHIDONG. BOUNDEDNESS AND EXPONENTIAL STABILITY FOR NONAUTONOMOUS FCNNs WITH REACTION-DIFFUSION TERMS AND TINE-VARYING DELAYS. INT J BIOMATH 2011. [DOI: 10.1142/s1793524511001143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, a class of nonautonomous fuzzy cellular neural networks (FCNNs) with reaction-diffusion terms and time-varying delays are investigated. By applying the inequality analysis technique, introducing ingeniously many real parameters and constructing new auxiliary functions, a series of new and useful criteria on the boundedness and globally exponential stability of solutions are established. The results obtained in this paper extend and improve the corresponding results given in previous works. Finally, two examples are given to verify the effectiveness of the obtained results.
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Affiliation(s)
- SHUYUN NIU
- National Center of ITS Engineering and Technology, Research Institute of Highway Ministry of Transport, Beijing, 100061, P. R. China
| | - HAIJUN JIANG
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, P. R. China
| | - ZHIDONG TENG
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, P. R. China
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35
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Wu Q, Zhou J, Xiang L. Impulses-induced exponential stability in recurrent delayed neural networks. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.05.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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36
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Delay-dependent exponential stability analysis for discrete-time switched neural networks with time-varying delay. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.01.015] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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37
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Li C, Li C, Liao X, Huang T. Impulsive effects on stability of high-order BAM neural networks with time delays. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2010.12.028] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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38
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Li C, Wu S, Feng GG, Liao X. Stabilizing Effects of Impulses in Discrete-Time Delayed Neural Networks. ACTA ACUST UNITED AC 2011; 22:323-9. [DOI: 10.1109/tnn.2010.2100084] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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39
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State estimation of recurrent neural networks with time-varying delay: A novel delay partition approach. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2010.10.006] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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40
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Zhanshan Wang, Huaguang Zhang, Ping Li. An LMI Approach to Stability Analysis of Reaction–Diffusion Cohen–Grossberg Neural Networks Concerning Dirichlet Boundary Conditions and Distributed Delays. ACTA ACUST UNITED AC 2010; 40:1596-606. [DOI: 10.1109/tsmcb.2010.2043095] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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41
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Niu S, Jiang H, Teng Z. Boundedness and exponential stability for nonautonomous FCNNs with distributed delays and reaction–diffusion terms. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2010.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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42
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Wu Z, Su H, Chu J. State estimation for discrete Markovian jumping neural networks with time delay. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2010.01.010] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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43
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Wu Z, Su H, Chu J, Zhou W. New results on robust exponential stability for discrete recurrent neural networks with time-varying delays. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2009.01.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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44
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Xia Y, Yang Z, Han M. Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification. ACTA ACUST UNITED AC 2009; 20:1165-80. [PMID: 19497816 DOI: 10.1109/tnn.2009.2016842] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.
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Affiliation(s)
- Yonghui Xia
- Department of Mathematics, Zhejiang Normal University, Jinhua 321004, China.
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45
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Liu B, Shi P. Delay-range-dependent stability for fuzzy BAM neural networks with time-varying delays. PHYSICS LETTERS A 2009; 373:1830-1838. [DOI: 10.1016/j.physleta.2009.03.044] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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46
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Jinling Liang, Zidong Wang, Xiaohui Liu. State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case. ACTA ACUST UNITED AC 2009; 20:781-93. [DOI: 10.1109/tnn.2009.2013240] [Citation(s) in RCA: 189] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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47
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Song Q, Liang J, Wang Z. Passivity analysis of discrete-time stochastic neural networks with time-varying delays. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2008.05.006] [Citation(s) in RCA: 138] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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48
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49
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Zhanshan Wang, Huaguang Zhang, Wen Yu. Robust Stability of Cohen–Grossberg Neural Networks via State Transmission Matrix. ACTA ACUST UNITED AC 2009; 20:169-74. [DOI: 10.1109/tnn.2008.2009119] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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50
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