A Coal Mine Gas Concentration Prediction Method based on Particle Swarm Optimization Support Vector Regression

摘要：

Gas concentration prediction plays an important role in the safety production of coal mine. Acceptable result has been achieved using Support Vector Regression to predict gas concentration;however, the efficiency of the algorithm is declined due to the parameter choice. This paper presents a coal mine gas concentration prediction method based on Particle Swarm Optimization Support Vector Regression which using Particle Swarm Optimization algorithm to optimize the parameters used in Support Vector Regression and improve the models accuracy and generalization ability. Experiments on the real coal mine gas concentration show that the proposed method is more accuracy than that without using Particle Swarm Optimization.

关键词： particle swarm optimization support vector regression gas concentration

作者: Yanli Chai

作者单位: School of information and electrical engineering CUMT Xuzhou, Jiangsu ,China

会议类型: 国际会议

会议名称: 2011 2nd International Conference on Data Storage and Data Engineering(DSDE 2011)(2011年第二届数据存储与数据工程国际会议)

会议地点: 西安

会议语种:英文

页码: 334-337

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）