Application of BP Neural Network into the Kow of Chemical Contaminants

Abstract:

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The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

574-578

DOI:

10.4028/www.scientific.net/AMR.121-122.574

Citation:

H. Y. Jiang et al., "Application of BP Neural Network into the Kow of Chemical Contaminants", Advanced Materials Research, Vols. 121-122, pp. 574-578, 2010

Online since:

June 2010

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Price:

$35.00

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