These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

178 related articles for article (PubMed ID: 34856528)

  • 1. Stability and dissipativity criteria for neural networks with time-varying delays via an augmented zero equality approach.
    Lee SH; Park MJ; Ji DH; Kwon OM
    Neural Netw; 2022 Feb; 146():141-150. PubMed ID: 34856528
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Extended Dissipativity Analysis for Markovian Jump Neural Networks With Time-Varying Delay via Delay-Product-Type Functionals.
    Lin WJ; He Y; Zhang CK; Wu M; Shen J
    IEEE Trans Neural Netw Learn Syst; 2019 Aug; 30(8):2528-2537. PubMed ID: 30605107
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals.
    Manivannan R; Samidurai R; Cao J; Alsaedi A; Alsaadi FE
    Neural Netw; 2017 Mar; 87():149-159. PubMed ID: 28152392
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Dissipativity Analysis for Neural Networks With Time-Varying Delays via a Delay-Product-Type Lyapunov Functional Approach.
    Lian HH; Xiao SP; Yan H; Yang F; Zeng HB
    IEEE Trans Neural Netw Learn Syst; 2021 Mar; 32(3):975-984. PubMed ID: 32275622
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Some Novel Results on Stability Analysis of Generalized Neural Networks With Time-Varying Delays via Augmented Approach.
    Kwon OM; Lee SH; Park MJ
    IEEE Trans Cybern; 2022 Apr; 52(4):2238-2248. PubMed ID: 32886616
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Global dissipativity analysis on uncertain neural networks with mixed time-varying delays.
    Song Q; Cao J
    Chaos; 2008 Dec; 18(4):043126. PubMed ID: 19123636
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Stability analysis of neutral type neural networks with mixed time-varying delays using triple-integral and delay-partitioning methods.
    Shi K; Zhu H; Zhong S; Zeng Y; Zhang Y; Wang W
    ISA Trans; 2015 Sep; 58():85-95. PubMed ID: 25835437
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays.
    Tu K; Xue Y; Zhang X
    Neural Netw; 2024 Jul; 175():106279. PubMed ID: 38608536
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Improvements on stability criteria for linear systems with a time-varying delay via novel delay-dependent Lyapunov functionals.
    Lee SH; Park MJ; Kwon OM
    ISA Trans; 2024 Sep; 152():269-276. PubMed ID: 38972823
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Global exponential stability of neural networks with time-varying delay based on free-matrix-based integral inequality.
    He Y; Ji MD; Zhang CK; Wu M
    Neural Netw; 2016 May; 77():80-86. PubMed ID: 26945439
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An Improved Result on Dissipativity and Passivity Analysis of Markovian Jump Stochastic Neural Networks With Two Delay Components.
    Nagamani G; Radhika T; Zhu Q
    IEEE Trans Neural Netw Learn Syst; 2017 Dec; 28(12):3018-3031. PubMed ID: 27740500
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Stability and dissipativity analysis of distributed delay cellular neural networks.
    Feng Z; Lam J
    IEEE Trans Neural Netw; 2011 Jun; 22(6):976-81. PubMed ID: 21558058
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dissipativity analysis of stochastic memristor-based recurrent neural networks with discrete and distributed time-varying delays.
    Radhika T; Nagamani G
    Network; 2016; 27(4):237-267. PubMed ID: 27385193
    [TBL] [Abstract][Full Text] [Related]  

  • 14. New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays.
    Zhang G; Zeng Z; Hu J
    Neural Netw; 2018 Jan; 97():183-191. PubMed ID: 29128703
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Delay-dependent Lurie-Postnikov type Lyapunov-Krasovskii functionals for stability analysis of discrete-time delayed neural networks.
    Xie KY; Zhang CK; Lee S; He Y; Liu Y
    Neural Netw; 2024 May; 173():106195. PubMed ID: 38394998
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Stability analysis of delayed neural networks via a new integral inequality.
    Yang B; Wang J; Wang J
    Neural Netw; 2017 Apr; 88():49-57. PubMed ID: 28189839
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Stability Analysis of Distributed Delay Neural Networks Based on Relaxed Lyapunov-Krasovskii Functionals.
    Zhang B; Lam J; Xu S
    IEEE Trans Neural Netw Learn Syst; 2015 Jul; 26(7):1480-92. PubMed ID: 25181489
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Improved delay-dependent robust stability criteria for recurrent neural networks with time-varying delays.
    Liu PL
    ISA Trans; 2013 Jan; 52(1):30-5. PubMed ID: 22959741
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach.
    Zhang XM; Han QL
    Neural Netw; 2014 Jun; 54():57-69. PubMed ID: 24650958
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Global robust dissipativity of interval recurrent neural networks with time-varying delay and discontinuous activations.
    Duan L; Huang L; Guo Z
    Chaos; 2016 Jul; 26(7):073101. PubMed ID: 27475061
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.