• DocumentCode
    2436969
  • Title

    Forecast the Biological Activity of Nitrobenzene Compound Based on BP Neural Network

  • Author

    HuiYu Jiang ; HuiYong Jiang ; Wei, Tao ; Yang, Feng

  • Author_Institution
    Dept. of Chem. Eng., Wuhan Univ. of Sci. & Eng., Wuhan
  • Volume
    2
  • fYear
    2008
  • fDate
    19-20 Dec. 2008
  • Firstpage
    85
  • Lastpage
    87
  • 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 network in 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 0.05 in this paper. So it is a good forecast mode of the nitrobenzene compound biological activity.
  • Keywords
    backpropagation; biology computing; chemical hazards; neural nets; nitrogen compounds; regression analysis; toxicology; Levenberg_Marquardt BP neural network; QSAR; biological activity; biological toxicity forecast; multivariate linear regression analysis; nitrobenzene compound; sample selectionjn; structure-activity relationship; Artificial neural networks; Biology; Chemical industry; Convergence; Feedforward systems; Linear regression; Neural networks; Neurons; Nonlinear equations; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3490-9
  • Type

    conf

  • DOI
    10.1109/PACIIA.2008.209
  • Filename
    4756740