• DocumentCode
    3006377
  • Title

    Application of BP Nerual Network into Prediction of Nitrobenzene Compound in Toxicity

  • Author

    Jiang, HuiYu ; Dong, Min ; Yang, Feng

  • Author_Institution
    Inst. of Chem. Eng., Wuhan Univ. of Sci. & Eng., Wuhan
  • fYear
    2008
  • fDate
    25-26 Sept. 2008
  • Firstpage
    170
  • Lastpage
    173
  • Abstract
    At present, the multivariate linear regression analysis was adopted in the biological toxicity forecast through establishment equation of the QSAR mostly, but the error forecasted was big in many situations because of the complexity and nonlinearity of structure-activity relationship, and it has a high request to the sample selection. In this paper forecast model of the nitrobenzene compound biological toxicity has been established based on the Levenberg-Marquardt BP neural networkin this paper. The studies suggest that the BP network has the strong misalignment to approach ability, the fitting precision is good between the output and the sample,the result is better using the BP network to forecast, the correlation coefficient has achieved 0.999, the prediction error in the permission scope, the biggest absolute value of error is 0.04 in this paper. So it is a good forecast mode of the nitrobenzene compound biological activity.
  • Keywords
    backpropagation; neural nets; organic compounds; physics computing; regression analysis; BP neural network; Levenberg-Marquardt BP neural network; biological toxicity; forecast model; multivariate linear regression analysis; nitrobenzene compound; structure-activity relationship; Artificial neural networks; Biological system modeling; Biology; Computer applications; Computer networks; Genetic engineering; Gradient methods; Linear regression; Neural networks; Nonlinear equations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Genetic and Evolutionary Computing, 2008. WGEC '08. Second International Conference on
  • Conference_Location
    Hubei
  • Print_ISBN
    978-0-7695-3334-6
  • Type

    conf

  • DOI
    10.1109/WGEC.2008.9
  • Filename
    4637420