Roughness prediction model for artificially roughened channel based on partial least square and least square support vector machine
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Abstract:
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.
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