research-article

Novel results on robust finite-time passivity for discrete-time delayed neural networks

Authors:

K. Mathiyalagan,

R. SakthivelAuthors Info & Claims

Neurocomputing, Volume 177, Issue C

Pages 585 - 593

https://doi.org/10.1016/j.neucom.2015.10.125

Published: 12 February 2016 Publication History

Abstract

This paper presents some novel results on robust finite-time passivity for a class of uncertain discrete-time neural networks (DNNs) with time varying delays. Using the Lyapunov theory together with the zero inequalities, convex combination and reciprocally convex combination approaches, we propose the sufficient conditions for finite-time boundedness and finite-time passivity of DNN for all admissible uncertainties. The results are achieved by using a new Lyapunov-Krasovskii functional (LKF) with novel triple summation terms, several delay-dependent criteria for the DNN are derived in terms of linear matrix inequalities (LMIs) which can be easily verified via the LMI toolbox. Finally, numerical example with simulation scheme have been presented to illustrate the applicability and usefulness of the obtained results.

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Cited By

Nie HZhang YSun J(2024)Finite-time passivity of multi-weighted coupled neural networks with directed topologies and time-varying delayNeurocomputing10.1016/j.neucom.2024.128581610:COnline publication date: 28-Dec-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.128581
Tu KXue YZhang X(2024)A new approach based on system solutions for passivity analysis of discrete-time memristor-based neural networks with time-varying delaysApplied Mathematics and Computation10.1016/j.amc.2024.128551469:COnline publication date: 15-May-2024
https://dl.acm.org/doi/10.1016/j.amc.2024.128551
Xiong LCai LCao JWu TZhang H(2023)Stochastic quantized control for memristive neural networks with mixed semi-Markov jump and sampled-data communications using a novel approachKnowledge-Based Systems10.1016/j.knosys.2023.110751277:COnline publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110751
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Novel results on robust finite-time passivity for discrete-time delayed neural networks
1. Mathematics of computing
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Published In

cover image Neurocomputing

Neurocomputing Volume 177, Issue C

February 2016

672 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 12 February 2016

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Cited By

Nie HZhang YSun J(2024)Finite-time passivity of multi-weighted coupled neural networks with directed topologies and time-varying delayNeurocomputing10.1016/j.neucom.2024.128581610:COnline publication date: 28-Dec-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.128581
Tu KXue YZhang X(2024)A new approach based on system solutions for passivity analysis of discrete-time memristor-based neural networks with time-varying delaysApplied Mathematics and Computation10.1016/j.amc.2024.128551469:COnline publication date: 15-May-2024
https://dl.acm.org/doi/10.1016/j.amc.2024.128551
Xiong LCai LCao JWu TZhang H(2023)Stochastic quantized control for memristive neural networks with mixed semi-Markov jump and sampled-data communications using a novel approachKnowledge-Based Systems10.1016/j.knosys.2023.110751277:COnline publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110751
Hui MWei CZhang JHo-Ching Iu HLuo NYao RBai L(2020)Finite-Time Projective Synchronization of Fractional-Order Memristive Neural Networks with Mixed Time-Varying DelaysComplexity10.1155/2020/41687052020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/4168705
Li XZhang WFang JLi H(2019)Finite-time synchronization of memristive neural networks with discontinuous activation functions and mixed time-varying delaysNeurocomputing10.1016/j.neucom.2019.02.051340:C(99-109)Online publication date: 7-May-2019
https://dl.acm.org/doi/10.1016/j.neucom.2019.02.051
Thuan MHuong DHong D(2019)New Results on Robust Finite-Time Passivity for Fractional-Order Neural Networks with UncertaintiesNeural Processing Letters10.1007/s11063-018-9902-950:2(1065-1078)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11063-018-9902-9
Chen YYang LXue A(2019)Finite-Time Passivity of Stochastic Markov Jump Neural Networks with Random Distributed Delays and Sensor NonlinearitiesCircuits, Systems, and Signal Processing10.1007/s00034-018-0978-238:6(2422-2444)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00034-018-0978-2
Huang YChen WWang J(2018)Finite-time passivity of delayed multi-weighted complex dynamical networks with different dimensional nodesNeurocomputing10.1016/j.neucom.2018.05.058312:C(74-89)Online publication date: 27-Oct-2018
https://dl.acm.org/doi/10.1016/j.neucom.2018.05.058
Lin XLiang KLi HJiao YNie J(2017)Finite-Time Stability and Stabilization for Continuous Systems with Additive Time-Varying DelaysCircuits, Systems, and Signal Processing10.1007/s00034-016-0443-z36:7(2971-2990)Online publication date: 1-Jul-2017
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Syed Ali MSaravanan SArik S(2016)Finite-time H∞ state estimation for switched neural networks with time-varying delaysNeurocomputing10.1016/j.neucom.2016.05.037207:C(580-589)Online publication date: 26-Sep-2016
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