Output Prediction Model in Fully Mechanized Mining Face Based on Support Vector Machine

被引：0

作者：

Li, Wanqing ^{[1
]}

Meng, Wenqing ^{[2
]}

Zhao, Yong ^{[1
]}

Xu, Shipeng ^{[1
]}

机构：

[1] HeBei Univ Engn, Sch Econ & Management, Handan, Peoples R China

[2] HeBei Univ Engn, Sch Civil Engn, Handan, Peoples R China

来源：

WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2009年

关键词：

Support Vector Machine; Fully Mechanized Mining Face; Prediction;

D O I：

10.1109/WKDD.2009.41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Support Vector Machine is a new machine learning technique developed on the basis of Statistical Learning Theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model by programming based on Mat lab, finally, compared with genetic neural network prediction model. It shows that SVM has a higher accuracy of prediction than GNN, which proved the validity and practicality of the model.

引用

页码：171 / +

页数：2

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