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Journal Abstract Search

173 related items for PubMed ID: 19775960

  • 1.
    ; . PubMed ID:
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  • 2. Iterative algorithm for joint zero diagonalization with application in blind source separation.
    Zhang WT, Lou ST.
    IEEE Trans Neural Netw; 2011 Jul; 22(7):1107-18. PubMed ID: 21659020
    [Abstract] [Full Text] [Related]

  • 3. Approximate joint diagonalization and geometric mean of symmetric positive definite matrices.
    Congedo M, Afsari B, Barachant A, Moakher M.
    PLoS One; 2014 Jul; 10(4):e0121423. PubMed ID: 25919667
    [Abstract] [Full Text] [Related]

  • 4. A new concept for separability problems in blind source separation.
    Theis FJ.
    Neural Comput; 2004 Sep; 16(9):1827-50. PubMed ID: 15265324
    [Abstract] [Full Text] [Related]

  • 5. On blind separability based on the temporal predictability method.
    Xie S, Zhou G, Yang Z, Fu Y.
    Neural Comput; 2009 Dec; 21(12):3519-31. PubMed ID: 19686063
    [Abstract] [Full Text] [Related]

  • 6. A network model for blind source extraction in various ill-conditioned cases.
    Li Y, Wang J.
    Neural Netw; 2005 Dec; 18(10):1348-56. PubMed ID: 16153800
    [Abstract] [Full Text] [Related]

  • 7. 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]

  • 8. Parameter incremental learning algorithm for neural networks.
    Wan S, Banta LE.
    IEEE Trans Neural Netw; 2006 Nov; 17(6):1424-38. PubMed ID: 17131658
    [Abstract] [Full Text] [Related]

  • 9. Comparison of blind source separation methods in fast somatosensory-evoked potential detection.
    Liu H, Chang CQ, Luk KD, Hu Y.
    J Clin Neurophysiol; 2011 Apr; 28(2):170-7. PubMed ID: 21399512
    [Abstract] [Full Text] [Related]

  • 10. 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; 15(6):435-43. PubMed ID: 16385633
    [Abstract] [Full Text] [Related]

  • 11. Stability analysis and the stabilization of a class of discrete-time dynamic neural networks.
    Patan K.
    IEEE Trans Neural Netw; 2007 May; 18(3):660-73. PubMed ID: 17526334
    [Abstract] [Full Text] [Related]

  • 12. Constructive approximation to multivariate function by decay RBF neural network.
    Hou M, Han X.
    IEEE Trans Neural Netw; 2010 Sep; 21(9):1517-23. PubMed ID: 20693108
    [Abstract] [Full Text] [Related]

  • 13. Toward the training of feed-forward neural networks with the D-optimum input sequence.
    Witczak M.
    IEEE Trans Neural Netw; 2006 Mar; 17(2):357-73. PubMed ID: 16566464
    [Abstract] [Full Text] [Related]

  • 14. Analysis of superimposed ultrasonic guided waves in long bones by the joint approximate diagonalization of eigen-matrices algorithm.
    Song X, Ta D, Wang W.
    Ultrasound Med Biol; 2011 Oct; 37(10):1704-13. PubMed ID: 21924208
    [Abstract] [Full Text] [Related]

  • 15. Implementing online natural gradient learning: problems and solutions.
    Wan W.
    IEEE Trans Neural Netw; 2006 Mar; 17(2):317-29. PubMed ID: 16566461
    [Abstract] [Full Text] [Related]

  • 16. Weighted rule based adaptive algorithm for simultaneously extracting generalized eigenvectors.
    Yang J, Zhao Y, Xi H.
    IEEE Trans Neural Netw; 2011 May; 22(5):800-6. PubMed ID: 21421433
    [Abstract] [Full Text] [Related]

  • 17. On neural blind separation with noise suppression and redundancy reduction.
    Karhunen J, Cichocki A, Kasprzak W, Pajunen P.
    Int J Neural Syst; 1997 Apr; 8(2):219-37. PubMed ID: 9327277
    [Abstract] [Full Text] [Related]

  • 18. Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm.
    Yi Z, Ye M, Lv JC, Tan KK.
    IEEE Trans Neural Netw; 2005 Nov; 16(6):1318-28. PubMed ID: 16342477
    [Abstract] [Full Text] [Related]

  • 19. Novel stability criteria for delayed cellular neural networks.
    Cao J, Wang J, Liao X.
    Int J Neural Syst; 2003 Oct; 13(5):367-75. PubMed ID: 14652876
    [Abstract] [Full Text] [Related]

  • 20. Global convergence of SMO algorithm for support vector regression.
    Takahashi N, Guo J, Nishi T.
    IEEE Trans Neural Netw; 2008 Jun; 19(6):971-82. PubMed ID: 18541498
    [Abstract] [Full Text] [Related]

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