基于遗传算法的支持向量机预测含能材料密度的研究  

Research on QSPR for energetic materials based on genetic algorithm support vector machine

作　　者：宗朝霞[1] 汤宏胜[1] 贺曼[2] 葛忠学[3] 来蔚鹏[3] 李华[1] 

机构地区：[1]西北大学化学与材料科学学院,陕西西安710069 [2]西北大学信息科学与技术学院,陕西西安710127 [3]西安近代化学研究所,陕西西安710065

出　　处：《计算机与应用化学》2009年第12期1529-1533,共5页Computers and Applied Chemistry

基　　金：国防973资助项目(No.61374xx);国家自然科学基金资助项目(No.20675063)

摘　　要：基于遗传算法(genetic algorithm,GA)的变量筛选和支持向量机(support vector machine,SVM),提出了一种改进的定量结构-性质相关(quantitative structure detonation relationship,QSPR)建模方法——遗传-支持向量机(GA-SVM),并用其建立含能材料的定量结构-爆轰性能关系(QSDR)模型,此外还应用标准SVM方法建立了QSDR模型,并用这2种模型进行呋咱系含能化合物密度的预测,随机选取85%化合物作为训练集,用来建立模型,其余化合物作为测试集来测试模型的预测能力。预测结果的交互检验的相关系数平方分别为0.9887和0.9885,平均相对误差分别为1.16%和2.12%,表明了2种建模方法的有效性。通过对2种模型的预测能力进行比较,GA-SVM方法建立的QSDR模型能更好地预测呋咱系含能化合物的密度,更利于实际应用。A modified method to develop quantitative structure property relationship （QSPR） models was proposed based on genetic al- gorithm （GA） and support vector machine （SVM） （GA-SVM）. GA was used to perform the variable selection, and SVM was used to construct QSPR model. GA-SVM was applied to develop the quantitative structure detonation relationship model for energetic materials. The standard SVM was also utilized to construct QSDR prediction models, 85% of the whole date set were taken as training set to construct the model, the rest compounds were used to test the prediction ability of the model. The cross-validation correlation coefficient Rcv^2 is 0. 988 7 and 0. 988 5, and the mean relative error is 1.16% and 2. 12% , for the prediction results respectively. It demonstrates the validity of these two methods. By comparison the stability with prediction ability of the models, it was found that GA-SVM is the optimal method for developing QSDR model for predicting the density of furazan compounds.

关 键 词：含能材料密度 支持向量机 遗传算法 定量结构-爆轰性能关系 

分 类 号：TQ015.9[化学工程] O6-39[理学—化学]