摘要
为了更加快速、精确地对混合生物质灰熔点进行预测,利用交叉验证(cross validation,CV)方法进一步优化了前人提出的经遗传算法(genetic algorithm,GA)优化的支持向量机(support vector machine,SVM)回归模型。以灰成分作为输入量,灰熔点为输出量,以单生物质数据训练该模型,对混合生物质灰熔点进行了预测;并与仅经GA优化模型的预测结果进行了比较。研究结果表明:经GA与CV优化的SVM模型对混合生物质灰熔点进行预测,平均绝对误差为25.0℃,平均相对误差为2.7%,比仅经GA优化的SVM模型预测结果更为精确;适当地设置相关参数可以节省程序运行时间。
In order to predict the ash fusion point of a mixed biomass more quickly and accurately, the support vector machine（SVM） regression model was optimized by a genetic algorithm（ GA）, built by other researchers was further optimized by cross validation（CV）. The ash fusion point of a mixed biomass was predicted by the optimized model and was trained by the data of a single biomass while taking ash compositions as input and the ash fusion point as output. The result was compared with models optimized only by GA. The results showed that the SVM model optimized by GA and CV, with average absolute error 25. 0 ℃ and relative error 2. 7%, could predict the ash fusion point of a mixed binmass better than that optimized only by GA, and the running time could be saved if parameters were properly set.
出处
《山东大学学报（工学版）》
CAS
北大核心
2012年第2期108-111,123,共5页
Journal of Shandong University（Engineering Science）
关键词
灰熔点
支持向量机
遗传算法
交叉验证
预测
ash fusion point
support vector machine
genetic algorithm
cross validation
prediction
