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Journal Abstract Search

309 related items for PubMed ID: 21504688

  • 1. Artificial neural networks applied to forecasting time series.
    Montaño Moreno JJ, Palmer Pol A, Muñoz Gracia P.
    Psicothema; 2011 Apr; 23(2):322-9. PubMed ID: 21504688
    [Abstract] [Full Text] [Related]

  • 2. Toward automatic time-series forecasting using neural networks.
    Yan W.
    IEEE Trans Neural Netw Learn Syst; 2012 Jul; 23(7):1028-39. PubMed ID: 24807130
    [Abstract] [Full Text] [Related]

  • 3. Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.
    Lu WZ, Wang WJ.
    Chemosphere; 2005 Apr; 59(5):693-701. PubMed ID: 15792667
    [Abstract] [Full Text] [Related]

  • 4. [Artificial neural network forecasting method in monitoring technique by spectrometric oil analysis].
    Yang YW, Chen G, Yang YW, Chen G.
    Guang Pu Xue Yu Guang Pu Fen Xi; 2005 Aug; 25(8):1339-43. PubMed ID: 16329517
    [Abstract] [Full Text] [Related]

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  • 6. Application of short-term water demand prediction model to Seoul.
    Joo CN, Koo JY, Yu MJ.
    Water Sci Technol; 2002 Aug; 46(6-7):255-61. PubMed ID: 12380999
    [Abstract] [Full Text] [Related]

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  • 8. Forecasting the prognosis of choroidal melanoma with an artificial neural network.
    Kaiserman I, Rosner M, Pe'er J.
    Ophthalmology; 2005 Sep; 112(9):1608. PubMed ID: 16023213
    [Abstract] [Full Text] [Related]

  • 9. Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.
    Lu WZ, Wang WJ, Wang XK, Yan SH, Lam JC.
    Environ Res; 2004 Sep; 96(1):79-87. PubMed ID: 15261787
    [Abstract] [Full Text] [Related]

  • 10. Application of artificial neural networks (ANN) in the development of solid dosage forms.
    Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H.
    Pharm Dev Technol; 1997 May; 2(2):111-21. PubMed ID: 9552437
    [Abstract] [Full Text] [Related]

  • 11. Artificial intelligence approach with the use of artificial neural networks for the creation of a forecasting model of Plasmopara viticola infection.
    Bugliosi R, Spera G, La Torre A, Campoli L, Scaglione M.
    Commun Agric Appl Biol Sci; 2006 May; 71(3 Pt A):859-65. PubMed ID: 17390832
    [Abstract] [Full Text] [Related]

  • 12. Evaluation of atmospheric Poaceae pollen concentration using a neural network applied to a coastal Atlantic climate region.
    Rodríguez-Rajo FJ, Astray G, Ferreiro-Lage JA, Aira MJ, Jato-Rodriguez MV, Mejuto JC.
    Neural Netw; 2010 Apr; 23(3):419-25. PubMed ID: 19604673
    [Abstract] [Full Text] [Related]

  • 13. Polynomial harmonic GMDH learning networks for time series modeling.
    Nikolaev NY, Iba H.
    Neural Netw; 2003 Dec; 16(10):1527-40. PubMed ID: 14622880
    [Abstract] [Full Text] [Related]

  • 14. Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction.
    Noori R, Karbassi A, Salman Sabahi M.
    J Environ Manage; 2010 Dec; 91(3):767-71. PubMed ID: 19913989
    [Abstract] [Full Text] [Related]

  • 15. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.
    Wang WC, Chau KW, Qiu L, Chen YB.
    Environ Res; 2015 May; 139():46-54. PubMed ID: 25684671
    [Abstract] [Full Text] [Related]

  • 16. Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach.
    Zaqoot HA, Ansari AK, Unar MA, Khan SH.
    Water Sci Technol; 2009 May; 60(12):3051-9. PubMed ID: 19955628
    [Abstract] [Full Text] [Related]

  • 17. An automated cervical pre-cancerous diagnostic system.
    Mat-Isa NA, Mashor MY, Othman NH.
    Artif Intell Med; 2008 Jan; 42(1):1-11. PubMed ID: 17996432
    [Abstract] [Full Text] [Related]

  • 18. An ANN application for water quality forecasting.
    Palani S, Liong SY, Tkalich P.
    Mar Pollut Bull; 2008 Sep; 56(9):1586-97. PubMed ID: 18635240
    [Abstract] [Full Text] [Related]

  • 19. [Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever].
    Zhu Y, Xia JL, Wang J.
    Zhonghua Liu Xing Bing Xue Za Zhi; 2009 Sep; 30(9):964-8. PubMed ID: 20193238
    [Abstract] [Full Text] [Related]

  • 20. A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance.
    Namikawa J, Tani J.
    Neural Netw; 2008 Dec; 21(10):1466-75. PubMed ID: 18938059
    [Abstract] [Full Text] [Related]

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