Using GA-ANN Algorithm to Predicate Coal Bump Energy

被引：0
|
作者：
Tan, Yunliang [1 ]
Zhao, Tongbin [1 ]
Zhao, Zhigang [1 ]
机构：
[1] Key Lab Mine Disaster Prevent, Qingdao, Peoples R China
来源：
WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09) | 2009年
关键词：
Coal bump; energy; genetic algorithm; artificial neural network; predication;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A GA-ANN network was constructed for preidcating coal bump energy, based on the 300 training samples form simulated results with PFC2D software for different coal particle stiffness. It was tested that the average relative error of fitted-output value is only 2.5%, the averagre relative error of generalized predicated output is only 8.4%. It is valuable for coal bump energy predication.
引用
收藏
页码：973 / 976
页数：4
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