Selection of Affecting Factors of Coal and Gas Outburst on Genetic Algorithm

摘要：

Genetic algorithm (GA) is applied to select main affecting factors of coal and gas outburst to solve the over-fitting problem of BP neural network (NN) in predicting coal and gas outburst, and a modified BP NN predictor is established, which input variables are the factors selected. In our GA, chromosome is a binary encoding which each gene corresponds to a variable, penalty function is introduced into fitness function. Finally, the method is studied using real samples of PingMei 8th mine in MATLAB2009b environment The results demonstrate that fitting effect and prediction accuracy of the modified BP NN predictor is improved significantly and simulation time is shorter after predictors input valuables are optimized on GA.

关键词： Coal and Gas Outburst Genetic Algorithm Variable Selection Neural Network MA TLAB

作者: HuiTao Mei-ying Qiao

作者单位: Henan Polytechnic University, Jiaozuo, Henan 454000, China China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

会议地点: 深圳

会议语种:英文

页码: 236-239

在线出版日期: 2011-03-28（万方平台首次上网日期，不代表论文的发表时间）