Prediction for Gas Emission Quantity of the Working Face Based on Improved-GA LS-SVM

摘要：

Gas emission quantity of the working face has important meanings for determining gas content and ventilation content of mine, even gas outburst. The paper puts forward a new method of forecasting gas emission quantity of the working face based on improved GA LS-SVM. The method can not only overcome over learning and the poor generalization ability, but also use the improved genetic algorithm to raise the preferences efficiency to avoid the disadvantage of manually specifying the parameters selected, and also scales down the optimization time. The experimental result proves that the method is appropriate for forecasting the gas emission quantity of the working face, and has major value of spread and exploitation.

关键词： genetic algorithm Least Squares Support Vector Machine working face gas emission quantity predict

作者: Yuxi Feng Kaizhi Zhang Jiming Zhu Fengzhen Liu

作者单位: Department of Economic Management,Shan Dong University of Science and Technology,Taian, China College of Resource and Environment,Shan Dong University of Science and Technology,Qingdao, China

会议类型: 国际会议

会议名称: 2012 International Conference on Electric Technology and Civil Engineering(2012 电子技术与土木工程国际会议 ICETCE 2012)

会议地点: 三峡

会议语种:英文

页码: 2558-2561

在线出版日期: 2012-05-18（万方平台首次上网日期，不代表论文的发表时间）