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Chen Y, Zhang N, Yang J. A survey of recent advances on stability analysis, state estimation and synchronization control for neural networks. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.10.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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2
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Moorthy S, Joo YH. Distributed leader-following formation control for multiple nonholonomic mobile robots via bioinspired neurodynamic approach. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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3
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Improved Results on Finite-Time Passivity and Synchronization Problem for Fractional-Order Memristor-Based Competitive Neural Networks: Interval Matrix Approach. FRACTAL AND FRACTIONAL 2022. [DOI: 10.3390/fractalfract6010036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This research paper deals with the passivity and synchronization problem of fractional-order memristor-based competitive neural networks (FOMBCNNs) for the first time. Since the FOMBCNNs’ parameters are state-dependent, FOMBCNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, the conventional robust control scheme cannot guarantee the synchronization of FOMBCNNs. Under the framework of the Filippov solution, the drive and response FOMBCNNs are first transformed into systems with interval parameters. Then, the new sufficient criteria are obtained by linear matrix inequalities (LMIs) to ensure the passivity in finite-time criteria for FOMBCNNs with mismatched switching jumps. Further, a feedback control law is designed to ensure the finite-time synchronization of FOMBCNNs. Finally, three numerical cases are given to illustrate the usefulness of our passivity and synchronization results.
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4
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Liu CG, Wang JL. Passivity of fractional-order coupled neural networks with multiple state/derivative couplings. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.05.050] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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5
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Yang B, Hao M, Han M, Zhao X, Zong G. Exponential Stability of Discrete-Time Neural Networks With Large Delay. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2824-2834. [PMID: 31329569 DOI: 10.1109/tcyb.2019.2923244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We study the exponential stability of discrete-time neural networks (NNs) with a time-varying delay which contains a few intermittent large delays (LDs). By modeling the considered discrete-time NN as a discrete-time switched NN which contains two subsystems and one of them may be unstable over the LD periods (LDPs), switching techniques are employed to analyze the problem. Delay-dependent exponential stability conditions to check the frequency and the length of the LDs allowed for guaranteeing the exponential stability are proposed by applying a novel Lyapunov-Krasovskii functional (LKF) with LDP-based terms, Wirtinger-based summation inequality, and reciprocally convex combination technique. Based on these conditions, associated evaluation algorithms are developed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.
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6
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Discrete-Time Stochastic Quaternion-Valued Neural Networks with Time Delays: An Asymptotic Stability Analysis. Symmetry (Basel) 2020. [DOI: 10.3390/sym12060936] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Stochastic disturbances often cause undesirable characteristics in real-world system modeling. As a result, investigations on stochastic disturbances in neural network (NN) modeling are important. In this study, stochastic disturbances are considered for the formulation of a new class of NN models; i.e., the discrete-time stochastic quaternion-valued neural networks (DSQVNNs). In addition, the mean-square asymptotic stability issue in DSQVNNs is studied. Firstly, we decompose the original DSQVNN model into four real-valued models using the real-imaginary separation method, in order to avoid difficulties caused by non-commutative quaternion multiplication. Secondly, some new sufficient conditions for the mean-square asymptotic stability criterion with respect to the considered DSQVNN model are obtained via the linear matrix inequality (LMI) approach, based on the Lyapunov functional and stochastic analysis. Finally, examples are presented to ascertain the usefulness of the obtained theoretical results.
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7
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Stability and Dissipativity Analysis for Neutral Type Stochastic Markovian Jump Static Neural Networks with Time Delays. JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH 2019. [DOI: 10.2478/jaiscr-2019-0003] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
This paper studies the global asymptotic stability and dissipativity problem for a class of neutral type stochastic Markovian Jump Static Neural Networks (NTSMJSNNs) with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii Functional (LKF) with some augmented delay-dependent terms and by using integral inequalities to bound the derivative of the integral terms, some new sufficient conditions have been obtained, which ensure that the global asymptotic stability in the mean square. The results obtained in this paper are expressed in terms of Strict Linear Matrix Inequalities (LMIs), whose feasible solutions can be verified by effective MATLAB LMI control toolbox. Finally, examples and simulations are given to show the validity and advantages of the proposed results.
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Wang L, Zeng Z, Ge MF, Hu J. Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays. Neural Netw 2018; 105:65-74. [DOI: 10.1016/j.neunet.2018.04.014] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/08/2018] [Accepted: 04/20/2018] [Indexed: 12/01/2022]
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10
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Ding S, Wang Z, Zhang H. Dissipativity Analysis for Stochastic Memristive Neural Networks With Time-Varying Delays: A Discrete-Time Case. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:618-630. [PMID: 28055917 DOI: 10.1109/tnnls.2016.2631624] [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
In this paper, the dissipativity problem of discrete-time memristive neural networks (DMNNs) with time-varying delays and stochastic perturbation is investigated. A class of logical switched functions are put forward to reflect the memristor-based switched property of connection weights, and the DMNNs are then recast into a tractable model. Based on the tractable model, the robust analysis method and Refined Jensen-based inequalities are applied to establish some sufficient conditions that ensure the of DMNNs. Two numerical examples are presented to illustrate the effectiveness of the obtained results.
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11
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New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays. Neural Netw 2018; 97:183-191. [DOI: 10.1016/j.neunet.2017.10.003] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Revised: 08/05/2017] [Accepted: 10/12/2017] [Indexed: 11/17/2022]
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12
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Improved criteria of delay-dependent stability for discrete-time neural networks with leakage delay. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.05.053] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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13
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Zhang CK, He Y, Jiang L, Wang QG, Wu M. Stability Analysis of Discrete-Time Neural Networks With Time-Varying Delay via an Extended Reciprocally Convex Matrix Inequality. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:3040-3049. [PMID: 28222008 DOI: 10.1109/tcyb.2017.2665683] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequality is developed to replace the popular reciprocally convex combination lemma (RCCL). It has potential to reduce the conservatism of the RCCL-based criteria without introducing any extra decision variable due to its advantage of reduced estimation gap using the same decision variables. Moreover, a delay-product-type term is introduced for the first time into the Lyapunov function candidate such that a delay-variation-dependent stability criterion with the bounds of delay change rate is established. Finally, the advantages of the proposed criteria are demonstrated through two numerical examples.
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14
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On passivity and robust passivity for discrete-time stochastic neural networks with randomly occurring mixed time delays. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2980-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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15
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Ren SY, Wu J, Xu BB. Passivity and pinning passivity of complex dynamical networks with spatial diffusion coupling. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.06.076] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Chen Y, Fu Z, Liu Y, Alsaadi FE. Further results on passivity analysis of delayed neural networks with leakage delay. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.045] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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17
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lin D, Vasilakos AV, Tang Y, Yao Y. Neural networks for computer-aided diagnosis in medicine: A review. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.08.039] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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18
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Ren SY, Wu J, Wei PC. Passivity and Pinning Passivity of Coupled Delayed Reaction–Diffusion Neural Networks with Dirichlet Boundary Conditions. Neural Process Lett 2016. [DOI: 10.1007/s11063-016-9557-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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19
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Delay-range-dependent passivity analysis for uncertain stochastic neural networks with discrete and distributed time-varying delays. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.12.056] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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20
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Lin DH, Wu J, Li JN. Less conservative stability condition for uncertain discrete-time recurrent neural networks with time-varying delays. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Nagamani G, Ramasamy S. Dissipativity and passivity analysis for uncertain discrete-time stochastic Markovian jump neural networks with additive time-varying delays. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.097] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Ahn CK, Shi P, Wu L. Receding Horizon Stabilization and Disturbance Attenuation for Neural Networks With Time-Varying Delay. IEEE TRANSACTIONS ON CYBERNETICS 2015; 45:2680-2692. [PMID: 25561601 DOI: 10.1109/tcyb.2014.2381604] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This paper is concerned with the problems of receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay. New delay-dependent conditions on the terminal weighting matrices of a new finite horizon cost functional for receding horizon stabilization are established for neural networks with time-varying or time-invariant delays using single- and double-integral Wirtinger-type inequalities. Based on the results, delay-dependent sufficient conditions for the receding horizon disturbance attenuation are given to guarantee the infinite horizon H∞ performance of neural networks with time-varying or time-invariant delays. Three numerical examples are provided to illustrate the effectiveness of the proposed approach.
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23
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Wei PC, Wang JL, Huang YL, Xu BB, Ren SY. Passivity analysis of impulsive coupled reaction-diffusion neural networks with and without time-varying delay. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.06.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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24
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Kang W, Zhong S, Cheng J. Relaxed passivity conditions for discrete-time stochastic delayed neural networks. INT J MACH LEARN CYB 2015. [DOI: 10.1007/s13042-015-0428-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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25
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Li Y, Huang Z. New Results on Passivity Analysis of Stochastic Neural Networks with Time-Varying Delay and Leakage Delay. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:389250. [PMID: 26366165 PMCID: PMC4542025 DOI: 10.1155/2015/389250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 06/23/2015] [Accepted: 06/25/2015] [Indexed: 11/18/2022]
Abstract
The passivity problem for a class of stochastic neural networks systems (SNNs) with varying delay and leakage delay has been further studied in this paper. By constructing a more effective Lyapunov functional, employing the free-weighting matrix approach, and combining with integral inequality technic and stochastic analysis theory, the delay-dependent conditions have been proposed such that SNNs are asymptotically stable with guaranteed performance. The time-varying delay is divided into several subintervals and two adjustable parameters are introduced; more information about time delay is utilised and less conservative results have been obtained. Examples are provided to illustrate the less conservatism of the proposed method and simulations are given to show the impact of leakage delay on stability of SNNs.
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Affiliation(s)
- YaJun Li
- Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan 528300, China
| | - Zhaowen Huang
- Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan 528300, China
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26
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He S. Non-fragile passive controller design for nonlinear Markovian jumping systems via observer-based controls. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.06.053] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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27
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H∞ state estimation for discrete-time delayed neural networks with randomly occurring quantizations and missing measurements. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.06.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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28
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Zhang G, Shen Y, Yin Q, Sun J. Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays. Neural Netw 2015; 61:49-58. [PMID: 25462633 DOI: 10.1016/j.neunet.2014.10.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 07/18/2014] [Accepted: 10/03/2014] [Indexed: 10/24/2022]
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29
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Passivity and passification of memristor-based complex-valued recurrent neural networks with interval time-varying delays. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.04.034] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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30
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Guo Z, Wang J, Yan Z. Passivity and passification of memristor-based recurrent neural networks with time-varying delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2014; 25:2099-2109. [PMID: 25330432 DOI: 10.1109/tnnls.2014.2305440] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This paper presents new theoretical results on the passivity and passification of a class of memristor-based recurrent neural networks (MRNNs) with time-varying delays. The casual assumptions on the boundedness and Lipschitz continuity of neuronal activation functions are relaxed. By constructing appropriate Lyapunov-Krasovskii functionals and using the characteristic function technique, passivity conditions are cast in the form of linear matrix inequalities (LMIs), which can be checked numerically using an LMI toolbox. Based on these conditions, two procedures for designing passification controllers are proposed, which guarantee that MRNNs with time-varying delays are passive. Finally, two illustrative examples are presented to show the characteristics of the main results in detail.
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31
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New results on passivity analysis of memristor-based neural networks with time-varying delays. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.05.032] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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32
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33
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Passivity analysis for uncertain discrete-time stochastic BAM neural networks with time-varying delays. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1545-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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34
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New delay-distribution-dependent stability analysis for discrete-time stochastic neural networks with randomly time-varying delays. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2011.11.035] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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35
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Zhu S, Shen Y. Robustness analysis for connection weight matrices of global exponential stable time varying delayed recurrent neural networks. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2013.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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36
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TSENG KH, TSAI JSH, LU CY. A DELAY-DEPENDENT APPROACH TO ROBUST PASSIVITY ANALYSIS FOR TAKAGI-SUGENO FUZZY UNCERTAIN RECURRENT NEURAL NETWORKS WITH MIXED INTERVAL TIME-VARYING DELAYS. INT J UNCERTAIN FUZZ 2013. [DOI: 10.1142/s0218488513500219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper deals with the passivity analysis problem for Takagu-Sugeno (T-S) fuzzy neural networks with mixed interval time-varying delays and uncertain parameters. The time delays comprise discrete and distributed interval time-varying delays and the uncertain parameters are norm-bounded. Delay-dependent sufficient conditions for the passivity problem are obtained by using Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique. The important feature of the results lies in that it does not make use of upper bounds to introduce some degree of conservativeness. Two illustrative examples are exploited in order to illustrate the effectiveness of the proposed design procedures.
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Affiliation(s)
- K. H. TSENG
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - J. S. H. TSAI
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - C. Y. LU
- Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan
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Wu ZG, Shi P, Su H, Chu J. Dissipativity analysis for discrete-time stochastic neural networks with time-varying delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2013; 24:345-355. [PMID: 24808309 DOI: 10.1109/tnnls.2012.2232938] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, the problem of dissipativity analysis is discussed for discrete-time stochastic neural networks with time-varying discrete and finite-distributed delays. The discretized Jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms, and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically stable in the mean square and strictly (Q, S, R)-y-dissipative, which is delay-dependent in the sense that it depends on not only the discrete delay but also the finite-distributed delay. Based on the dissipativity criterion, some special cases are also discussed. Compared with the existing ones, the merit of the proposed results in this paper lies in their reduced conservatism and less decision variables. Three examples are given to illustrate the effectiveness and benefits of our theoretical results.
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38
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Robustness analysis for connection weight matrix of global exponential stability recurrent neural networks. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.08.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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39
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Robust stability and robust passivity of parabolic complex networks with parametric uncertainties and time-varying delays. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.02.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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40
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Shi G, Ma Q, Qu Y. Robust passivity analysis of a class of discrete-time stochastic neural networks. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-0838-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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41
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Jin-Liang Wang, Huai-Ning Wu, Lei Guo. Passivity and Stability Analysis of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions. ACTA ACUST UNITED AC 2011; 22:2105-16. [DOI: 10.1109/tnn.2011.2170096] [Citation(s) in RCA: 85] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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42
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Feng J, Wang S, Wang Z. Stochastic synchronization in an array of neural networks with hybrid nonlinear coupling. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.07.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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43
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Zheng-Guang Wu, Peng Shi, Hongye Su, Jian Chu. Passivity Analysis for Discrete-Time Stochastic Markovian Jump Neural Networks With Mixed Time Delays. ACTA ACUST UNITED AC 2011; 22:1566-75. [DOI: 10.1109/tnn.2011.2163203] [Citation(s) in RCA: 323] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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44
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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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45
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Wang H, Song Q. Synchronization for an array of coupled stochastic discrete-time neural networks with mixed delays. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.01.014] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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46
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Zhu S, Shen Y. Passivity analysis of stochastic delayed neural networks with Markovian switching. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.02.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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47
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Balasubramaniam P, Nagamani G. A delay decomposition approach to delay-dependent passivity analysis for interval neural networks with time-varying delay. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.01.011] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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48
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Hongyi Li, Huijun Gao, Peng Shi. New Passivity Analysis for Neural Networks With Discrete and Distributed Delays. ACTA ACUST UNITED AC 2010; 21:1842-7. [DOI: 10.1109/tnn.2010.2059039] [Citation(s) in RCA: 150] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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49
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Li H, Wang C, Shi P, Gao H. New passivity results for uncertain discrete-time stochastic neural networks with mixed time delays. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2010.04.019] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Global Passivity Analysis of Interval Neural Networks with Discrete and Distributed Delays of Neutral Type. Neural Process Lett 2010. [DOI: 10.1007/s11063-010-9147-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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