Optimization of Neural Networks Architecture for Impact Sensitivity of Energetic Molecules 


Vol. 26,  No. 3, pp. 399-408, Mar.  2005
10.5012/bkcs.2005.26.3.399


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  Abstract

We have utilized neural network (NN) studies to predict impact sensitivities of various types of explosive molecules. Two hundreds and thirty four explosive molecules have been taken from a single database, and thirty nine molecular descriptors were computed for each explosive molecule. Optimization of NN architecture has been carried out by examining seven different sets of molecular descriptors and varying the number of hidden neurons. For the optimized NN architecture, we have utilized 17 molecular descriptors which were composed of compositional and topological descriptors in an input layer, and 2 hidden neurons in a hidden layer.

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  Cite this article

[IEEE Style]

S. G. Cho, K. T. No, E. M. Goh, J. K. Kim, J. H. Shin, Y. D. Joo, S. Seong, "Optimization of Neural Networks Architecture for Impact Sensitivity of Energetic Molecules," Bulletin of the Korean Chemical Society, vol. 26, no. 3, pp. 399-408, 2005. DOI: 10.5012/bkcs.2005.26.3.399.

[ACM Style]

Soo Gyeong Cho, Kyoung Tai No, Eun Mee Goh, Jeong Kook Kim, Jae Hong Shin, Young Dae Joo, and Seeyearl Seong. 2005. Optimization of Neural Networks Architecture for Impact Sensitivity of Energetic Molecules. Bulletin of the Korean Chemical Society, 26, 3, (2005), 399-408. DOI: 10.5012/bkcs.2005.26.3.399.

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ISSN(Print) 0253-2964
ISSN(Online) 1229-5949