| 煤与瓦斯突出预报数据关联性的聚类分析 |
| |
| 引用本文： | 孙继平,李迎春,付兴建.煤与瓦斯突出预报数据关联性的聚类分析[J].湖南科技大学学报(自然科学版),2006,21(4):1-4. |
| |
| 作者姓名： | 孙继平 李迎春 付兴建 |
| |
| 作者单位： | 1. 中国矿业大学(北京校区)机电与信息工程学院,北京,100083
2. 北京信息科技大学,计算机与自动化系,北京,100085 |
| |
| 基金项目： | 高等学校博士学科点专项科研基金资助项目（20050290010） |
| |
| 摘 要： | 合理地选择突出控制因素是进行突出预测的关键技术之一．首先给出了基于系统聚类的突出预报数据关联性分析方法，通过DB Index准则判断聚类模型的有效性．然后研究了煤与瓦斯突出控制因素的选择规则．最后以平顶山煤矿为例，分析了该矿突出预报数据间的关联性，得到了相应的变量聚类树，并选择了主要的突出控制因素．仿真结果验证了所提出的突出控制因素选择方法的合理性和有效性．图2，表3，参11．
|
| 关 键 词： | 煤与瓦斯突出 控制因素 系统聚类算法 DB Index准则 聚类树 |
| 文章编号： | 1672-9102（2006）04-0001-04 |
| 收稿时间： | 2006-08-31 |
| 修稿时间： | 2006年8月31日 |
Clustering Analysis for Correlation among Coal and Gas Outburst's Prediction Data |
| |
| SUN Ji-ping,LI Ying-chun,FU Xing-jian.Clustering Analysis for Correlation among Coal and Gas Outburst''''s Prediction Data[J].Journal of Hunan University of Science & Technology(Natural Science Editon),2006,21(4):1-4. |
| |
| Authors: | SUN Ji-ping LI Ying-chun FU Xing-jian |
| |
| Institution: | 1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology , Beijing 100083, China 2. Department of Computer and Automation, Information Science and Technology University, Beijing 100085, China |
| |
| Abstract: | Control factors' selection was one of valuable technologies for the prediction of coal and gas outburst.Analysis method for correlation among coal and gas outburst's prediction data,which was based on system clustering algorithm,was presented first.And clustering models' validity was estimated by DB Index criterion.Then selective rules for coal and gas outburst's control factors were studied.At last,taking Pingdingsan coal mine as an example,correlation among outburst's prediction data was counted.And the corresponding regression tree was got.The main control factors of coal and gas outburst were selected.Simulation results illustrate that the proposed method is validity.2figs.,3tabs.,11refs. |
| |
| Keywords: | coal and gas outburst control factors system clustering algorithm DB Index criterion regression trees |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
