CHINESE JOURNAL OF ENERGETIC MATERIALS
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基于遗传算法的人工神经网络预测多硝基化合物撞击感度
作者:
作者单位:

(南京理工大学化工学院, 江苏 南京 210094)

作者简介:

钱博文(1992-),男,硕士生,主要从事热安全研究。e-mail: 897189968@qq.com 通信联系人: 陈利平(1981-),女,讲师,主要从事热安全研究。e-mail: 30629372@qq.com

通讯作者:

陈利平(1981-),女,讲师,主要从事热安全研究。e-mail: 30629372@qq.com

基金项目:


Prediction of Impact Sensitivity of Polynitro Compounds by Artificial Neural Network Based on the Genetic Algorithm
Author:
Affiliation:

(School of Chemical Engineering, Nanjing University of Science and Technology,Nanjing 210094, China)

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    摘要:

    为获得精度较高、适用范围较大的多硝基化合物撞击感度预测模型,根据定量构效关系(QSPR)原理,用遗传算法(GA)进行变量筛选,建立了多元线性回归(GA-MLR)模型和人工神经网络(GA-ANN)模型,用于149种多硝基化合物撞击感度的定量构效关系的研究。两个模型的相关系数分别为0.854和0.974,均方根误差分别为0.195和0.071,平均绝对误差分别为0.157和0.051。通过模型比较,结果表明所得模型的预测精度均高于已有的QSPR模型,且GA-ANN模型要明显优于GA-MLR模型。

    Abstract:

    To obtain the prediction model with higher precision and larger suitable scope for predicting impact sensitivity of polynitro compounds, based on the quantitative structure-property relationship (QSPR) principle, genetic algorithm (GA) was adopted in variable selection and GA-multiple linear regression (MLR) model and GA-artificial neural networks(ANN) model were established and employed in research for figuring out the relationship between impact sensitivity and molecular structure of 149 kinds of polynitro compounds. Two model′s correlation coefficient is 0.854 and 0.974, respectively, the root-mean-square error is 0.195 and 0.071, respectively and the mean-absolute-error is 0.157 and 0.051, respectively. By comparing the models, the results show that the prediction precision of two models are higher than the existing QSPR model, and GA-ANN model is obviously superior to GA-MLR model.

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引用本文

钱博文,陈利平,陈网桦.基于遗传算法的人工神经网络预测多硝基化合物撞击感度[J].含能材料, 2016, 24(7):644-650. DOI:10.11943/j. issn.1006-9941.2016.07.004.
QIAN Bo-wen, CHEN Li-ping, CHEN Wang-hua. Prediction of Impact Sensitivity of Polynitro Compounds by Artificial Neural Network Based on the Genetic Algorithm[J]. Chinese Journal of Energetic Materials, 2016, 24(7):644-650. DOI:10.11943/j. issn.1006-9941.2016.07.004.

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历史
  • 收稿日期: 2015-10-19
  • 最后修改日期: 2015-12-10
  • 录用日期: 2016-01-29
  • 在线发布日期: 2016-06-28
  • 出版日期: 2016-07-19