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 *

312 related articles for article (PubMed ID: 18252630)

  • 1. Inverting feedforward neural networks using linear and nonlinear programming.
    Lu BL; Kita H; Nishikawa Y
    IEEE Trans Neural Netw; 1999; 10(6):1271-90. PubMed ID: 18252630
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Converting general nonlinear programming problems into separable programming problems with feedforward neural networks.
    Lu BL; Ito K
    Neural Netw; 2003 Sep; 16(7):1059-74. PubMed ID: 14692639
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Three learning phases for radial-basis-function networks.
    Schwenker F; Kestler HA; Palm G
    Neural Netw; 2001 May; 14(4-5):439-58. PubMed ID: 11411631
    [TBL] [Abstract][Full Text] [Related]  

  • 4. An iterative pruning algorithm for feedforward neural networks.
    Castellano G; Fanelli AM; Pelillo M
    IEEE Trans Neural Netw; 1997; 8(3):519-31. PubMed ID: 18255656
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.
    Vuković N; Miljković Z
    Neural Netw; 2013 Oct; 46():210-26. PubMed ID: 23811384
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A novel neural network for nonlinear convex programming.
    Gao XB
    IEEE Trans Neural Netw; 2004 May; 15(3):613-21. PubMed ID: 15384549
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A high-performance feedback neural network for solving convex nonlinear programming problems.
    Leung Y; Chen KZ; Gao XB
    IEEE Trans Neural Netw; 2003; 14(6):1469-77. PubMed ID: 18244592
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application.
    Li S; Li Y; Wang Z
    Neural Netw; 2013 Mar; 39():27-39. PubMed ID: 23334164
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Neural networks for feedback feedforward nonlinear control systems.
    Parisini T; Zoppoli R
    IEEE Trans Neural Netw; 1994; 5(3):436-49. PubMed ID: 18267810
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Neural networks for nonlinear and mixed complementarity problems and their applications.
    Dang C; Leung Y; Gao XB; Chen KZ
    Neural Netw; 2004 Mar; 17(2):271-83. PubMed ID: 15036344
    [TBL] [Abstract][Full Text] [Related]  

  • 11. EEG source localization: comparative study of classical and neural network methods.
    Abeyratne UR; Zhang G; Saratchandran P
    Int J Neural Syst; 2001 Aug; 11(4):349-59. PubMed ID: 11706410
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A new recurrent neural network for solving convex quadratic programming problems with an application to the k-winners-take-all problem.
    Hu X; Zhang B
    IEEE Trans Neural Netw; 2009 Apr; 20(4):654-64. PubMed ID: 19228555
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A spectral clustering approach to underdetermined postnonlinear blind source separation of sparse sources.
    Van Vaerenbergh S; Santamaría I
    IEEE Trans Neural Netw; 2006 May; 17(3):811-4. PubMed ID: 16722185
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks.
    Patra JC; Kot AC
    IEEE Trans Syst Man Cybern B Cybern; 2002; 32(4):505-11. PubMed ID: 18238146
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Parametric signal restoration using artificial neural networks.
    Materka A; Mizushina S
    IEEE Trans Biomed Eng; 1996 Apr; 43(4):357-72. PubMed ID: 8626185
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A new neural network model for solving random interval linear programming problems.
    Arjmandzadeh Z; Safi M; Nazemi A
    Neural Netw; 2017 May; 89():11-18. PubMed ID: 28254557
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A new one-layer neural network for linear and quadratic programming.
    Gao X; Liao LZ
    IEEE Trans Neural Netw; 2010 Jun; 21(6):918-29. PubMed ID: 20388594
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A novel recurrent neural network with finite-time convergence for linear programming.
    Liu Q; Cao J; Chen G
    Neural Comput; 2010 Nov; 22(11):2962-78. PubMed ID: 20804382
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A hybrid linear/nonlinear training algorithm for feedforward neural networks.
    McLoone S; Brown MD; Irwin G; Lightbody A
    IEEE Trans Neural Netw; 1998; 9(4):669-84. PubMed ID: 18252490
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A new gradient-based neural network for solving linear and quadratic programming problems.
    Leung Y; Chen KZ; Jiao YC; Gao XB; Leung KS
    IEEE Trans Neural Netw; 2001; 12(5):1074-83. PubMed ID: 18249935
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.