| 基于PCA_Fuzzy_PSO_SVC的底板突水危险性评价 |
| |
| 引用本文： | 施龙青,谭希鹏,王娟,季小凯,牛超,徐东晶.基于PCA_Fuzzy_PSO_SVC的底板突水危险性评价[J].煤炭学报,2015,40(1):167-171. |
| |
| 作者姓名： | 施龙青 谭希鹏 王娟 季小凯 牛超 徐东晶 |
| |
| 作者单位： | 1.山东科技大学 山东省沉积成矿作用与沉积矿产重点实验室，山东 青岛 266590；;
2.山东科技大学 地球科学与工程学院，山东 青岛 266590 |
| |
| 基金项目： | 教育部高等学校博士学科点专项科研基金资助项目(20133718110004);山东省自然科学基金重点资助项目(ZR2011EEZ002);山东科技大学科研创新团队支持计划资助项目(2012KYTD101) |
| |
| 摘 要： | 为解决煤层底板突水预测难题，提出了基于主成分分析、模糊数学、粒子群算法以及支持向量机分类的底板突水危险性评价模型，模型以支持向量机分类为基础，通过主成分分析将多种影响底板突水的因子归纳为构造主成分、水文地质主成分、煤层信息主成分及开采条件主成分，其中构造主成分及水文地质主成分为影响底板是否突水的最主要控制因素，模糊化主成分因子，利用粒子群算法优化支持向量机分类参数，根据已有数据资料建立了评价模型，并将该模型应用于实际中，得到了准确的预测结果，为底板突水危险性评价提供了一种新的方法。
|
| 关 键 词： | 底板突水 危险性评价 主成分分析 模糊数学 粒子群算法 支持向量机 |
| 收稿时间： | 2014-03-21 |
Risk assessment of water inrush based on PCA_Fuzzy_PSO_SVC |
| |
| SHI Long-qing,TAN Xi-peng,WANG Juan,JI Xiao-kai,NIU Chao,XU Dong-jing.Risk assessment of water inrush based on PCA_Fuzzy_PSO_SVC[J].Journal of China Coal Society,2015,40(1):167-171. |
| |
| Authors: | SHI Long-qing TAN Xi-peng WANG Juan JI Xiao-kai NIU Chao XU Dong-jing |
| |
| Affiliation: | SHI Long-qing;TAN Xi-peng;WANG Juan;JI Xiao-kai;NIU Chao;XU Dong-jing;Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals,Shandong University of Science and Technology;College of Geological Sciences & Engineering,Shandong University of Science and Technology; |
| |
| Abstract: | To overcome the difficulty in forecasting the water inrush risk accurately，a model based on principal component analysis，fuzzy，particle swarm optimization algorithm and support vector machine was put forward.Six factors were reduced to the structure-principal components，the hydrogeology-principal components，the coal information-principal components and the mining conditions-principal components by the method called principal component analysis.Taking advantage of the fuzzy to handle the principal components and optimize the support vector machine’s parameters by particle swarm optimization algorithm，the model was established.Several cases were forecasted using the model and the results were excellent.The study proves that the model can precisely evaluate the risk of water inrush. |
| |
| Keywords: | water inrush risk assessment principal component analysis fuzzy particle swarm optimization algorithm support vector machine |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《煤炭学报》浏览原始摘要信息 |
|
点击此处可从《煤炭学报》下载全文 |
