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Journal Abstract Search

322 related items for PubMed ID: 16135404

  • 1. Stability analysis of a three-term backpropagation algorithm.
    Zweiri YH, Seneviratne LD, Althoefer K.
    Neural Netw; 2005 Dec; 18(10):1341-7. PubMed ID: 16135404
    [Abstract] [Full Text] [Related]

  • 2. New learning automata based algorithms for adaptation of backpropagation algorithm parameters.
    Meybodi MR, Beigy H.
    Int J Neural Syst; 2002 Feb; 12(1):45-67. PubMed ID: 11852444
    [Abstract] [Full Text] [Related]

  • 3. Convergence analysis of a simple minor component analysis algorithm.
    Peng D, Yi Z, Luo W.
    Neural Netw; 2007 Sep; 20(7):842-50. PubMed ID: 17765471
    [Abstract] [Full Text] [Related]

  • 4. On adaptive learning rate that guarantees convergence in feedforward networks.
    Behera L, Kumar S, Patnaik A.
    IEEE Trans Neural Netw; 2006 Sep; 17(5):1116-25. PubMed ID: 17001974
    [Abstract] [Full Text] [Related]

  • 5. TAO-robust backpropagation learning algorithm.
    Pernía-Espinoza AV, Ordieres-Meré JB, Martínez-de-Pisón FJ, González-Marcos A.
    Neural Netw; 2005 Mar; 18(2):191-204. PubMed ID: 15795116
    [Abstract] [Full Text] [Related]

  • 6. Adaptive improved natural gradient algorithm for blind source separation.
    Liu JQ, Feng DZ, Zhang WW.
    Neural Comput; 2009 Mar; 21(3):872-89. PubMed ID: 18928362
    [Abstract] [Full Text] [Related]

  • 7. An H(∞) control approach to robust learning of feedforward neural networks.
    Jing X.
    Neural Netw; 2011 Sep; 24(7):759-66. PubMed ID: 21458228
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  • 8. Magnified gradient function with deterministic weight modification in adaptive learning.
    Ng SC, Cheung CC, Leung SH.
    IEEE Trans Neural Netw; 2004 Nov; 15(6):1411-23. PubMed ID: 15565769
    [Abstract] [Full Text] [Related]

  • 9. Global convergence of online BP training with dynamic learning rate.
    Zhang R, Xu ZB, Huang GB, Wang D.
    IEEE Trans Neural Netw Learn Syst; 2012 Feb; 23(2):330-41. PubMed ID: 24808511
    [Abstract] [Full Text] [Related]

  • 10. Efficient calculation of the Gauss-Newton approximation of the Hessian matrix in neural networks.
    Fairbank M, Alonso E.
    Neural Comput; 2012 Mar; 24(3):607-10. PubMed ID: 22168563
    [Abstract] [Full Text] [Related]

  • 11. Learning in fully recurrent neural networks by approaching tangent planes to constraint surfaces.
    May P, Zhou E, Lee CW.
    Neural Netw; 2012 Oct; 34():72-9. PubMed ID: 22842197
    [Abstract] [Full Text] [Related]

  • 12. [Extended Kalman filtering trained neural networks and multicomponent analysis of amino acids].
    Li Z, Matsumoto S, Yu B, Sakai M, Li ML.
    Guang Pu Xue Yu Guang Pu Fen Xi; 1997 Jun; 17(3):123-6. PubMed ID: 15810234
    [Abstract] [Full Text] [Related]

  • 13. Evolutionary product unit based neural networks for regression.
    Martínez-Estudillo A, Martínez-Estudillo F, Hervás-Martínez C, García-Pedrajas N.
    Neural Netw; 2006 May; 19(4):477-86. PubMed ID: 16481148
    [Abstract] [Full Text] [Related]

  • 14. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.
    Lin WQ, Jiang JH, Zhou YP, Wu HL, Shen GL, Yu RQ.
    J Comput Chem; 2007 Jan 30; 28(2):519-27. PubMed ID: 17186488
    [Abstract] [Full Text] [Related]

  • 15. A self-stabilizing MSA algorithm in high-dimension data stream.
    Kong X, Hu C, Han C.
    Neural Netw; 2010 Sep 30; 23(7):865-71. PubMed ID: 20452742
    [Abstract] [Full Text] [Related]

  • 16. A modified error backpropagation algorithm for complex-value neural networks.
    Chen X, Tang Z, Variappan C, Li S, Okada T.
    Int J Neural Syst; 2005 Dec 30; 15(6):435-43. PubMed ID: 16385633
    [Abstract] [Full Text] [Related]

  • 17. Evolving logic networks with real-valued inputs for fast incremental learning.
    Park MS, Choi JY.
    IEEE Trans Syst Man Cybern B Cybern; 2009 Feb 30; 39(1):254-67. PubMed ID: 19068435
    [Abstract] [Full Text] [Related]

  • 18. Significant vector learning to construct sparse kernel regression models.
    Gao J, Shi D, Liu X.
    Neural Netw; 2007 Sep 30; 20(7):791-8. PubMed ID: 17604953
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  • 19. A learning rule for very simple universal approximators consisting of a single layer of perceptrons.
    Auer P, Burgsteiner H, Maass W.
    Neural Netw; 2008 Jun 30; 21(5):786-95. PubMed ID: 18249524
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  • 20. Convergence analysis of three classes of split-complex gradient algorithms for complex-valued recurrent neural networks.
    Xu D, Zhang H, Liu L.
    Neural Comput; 2010 Oct 30; 22(10):2655-77. PubMed ID: 20608871
    [Abstract] [Full Text] [Related]

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