%0 Journal Article
%T 基于偏最小二乘及最小二乘支持向量机的 人工加糙渠道糙率预测模型
%A 吴 思
%A 吴洋锋
%A 葛 赛
%A 赵 涛
%J 南水北调与水利科技
%D 2018
%X 影响渠道糙率的因素相当复杂, 且因素间又存在一定的相关关系。为取得更为精确的糙率预测效果, 采用偏 最小二乘( PLS) 法对影响人工加糙渠道糙率的因素进行分析, 提取影响自变量的重要成分, 结合最小二乘支持向量 机( LSSVM) 建立了人工加糙渠道糙率预测模型。结合实例, 通过对某人工加糙渠道相关试验数据进行PLS2LSSVM 模型的训练及预测, 并将预测结果与单独使用PLS、LSSVM 及公式法的预测结果进行对比, 其结果显示: 基于PLS2 LSSVM 模型的预测平均绝对百分比误差MAP E 为11 38%, 均方根误差RMSE 为21 24 @ 10- 4 , 预测精度均优于 PLS、LSSVM 及公式法的预测结果。结果表明, 将PLS 与LSSVM 相结合的PLS2LSSVM 模型, 综合了PLS 与 LSSVM 各自的优势, 应用PLS2LSSVM 模型可有效进行人工加糙渠道糙率的预测。  The fact ors that affect the roug hness o f a channel are quite complex , and there is a cer tain co rr elatio n between the fac2 to rs. In o rder to o bt ain a more accurate prediction of t he r oughness, we used the pa rtial least squares ( PLS) method to analyze the facto rs that affect the r oughness o f ar tificially r oughened channels, and we extr acted t he import ant compo nents that affect the independent v ariables. Then w e established the ro ug hness predictio n model for artif icially ro ug hened channels based on least squar e suppo rt v ect or machine ( LSSVM) . We used the ex per imental data of an ar tificially roug hened channel fo r training and pr ediction of the PLS2LSSVM mo del, and compar ed the pr edict ion results w ith the pr edict ion results of PLS, LSSVM, and for2 mula metho ds. T he results showed that the mean absolute percentag e er ro r (MA PE) o f predictio n based o n PLS2LSSVM model was 11 38%, and t he r oot mean square er ro r ( RMSE) was 21 24 @ 1024 . Its predictio n accuracy w as better than t hat o f t he PLS, LSSVM, and fo rmula methods. The results show ed that the PLS2LSSVM mo del w hich combines PLS and LSSVM can int eg rat e the advantages of PLS and LSSVM. PLS2LSSVM model can effect ively pr edict the roug hness of ar tificially roug hened channels.  新疆维吾尔自治区自然科学基金项目( 2015211A025)
%K 偏最小二乘( PLS)
%K 最小二乘支持向量机( LSSVM)
%K 人工加糙渠道
%K 糙率
%K 预测
%K par tial least squa res ( PLS)
%K least squar e suppor t v ecto r machine ( LSSVM)
%K art ificially r oughened channel
%K r ough2 ness
%K prediction
%U http://www.nsbdqk.net/nsbdyslkj/article/abstract/20180425?st=article_issue