Intelligent Prediction of Sieving Efficiency in Vibrating Screens

被引：13

作者：

Zhang, Bin ^{[1
,2
]}

Gong, Jinke ^{[1
]}

Yuan, Wenhua ^{[2
]}

Fu, Jun ^{[2
]}

Huang, Yi ^{[1
]}

机构：

[1] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China

[2] Shaoyang Univ, Dept Mech & Energy Engn, Shaoyang 422004, Peoples R China

来源：

SHOCK AND VIBRATION | 2016年 / 2016卷

基金：

美国国家科学基金会;

关键词：

SUPPORT; OPTIMIZATION; DESIGN;

D O I：

10.1155/2016/9175417

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.

引用

页数：7

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