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首页> 外文期刊>Journal of Chemical Information and Computer Sciences >PREDICTION OF THE IMPACT SENSITIVITY BY NEURAL NETWORKS
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PREDICTION OF THE IMPACT SENSITIVITY BY NEURAL NETWORKS

机译：通过神经网络预测冲击敏感性

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摘要

A method for optimizing the prediction of impact sensitivity of explosive molecules by neural networks is presented. The database consists of 204 molecules of known sensitivity, containing C, H, N, and O and belonging to several chemical families. Pertinent molecular descriptors are selected by a preliminary evolutionary multiple linear regression treatment, and the effects of the network's topology and the extent of the training are examined and optimized. The predictions are satisfactory with a correlation coefficient R = 0.94 obtained through cross-validation, The neural networks approach proves more accurate than linear methods and more general than all previously used methods. [References: 37]
机译：提出了一种通过神经网络优化炸药分子撞击敏感性预测的方法。该数据库由灵敏度已知的204个分子组成，包含C，H，N和O，并且属于几个化学家族。通过初步的进化多元线性回归处理选择相关的分子描述符，并检查和优化网络拓扑的影响以及训练的程度。通过交叉验证获得的相关系数R = 0.94，该预测令人满意。神经网络方法比线性方法更准确，比所有以前使用的方法更通用。 [参考：37]

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