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| 基于RS-SVM模型的煤与瓦斯突出多因素风险评价 | |
| 引用本文： | 刘俊娥,曾凡雷,郭章林.基于RS-SVM模型的煤与瓦斯突出多因素风险评价[J].中国安全科学学报,2011,21(7). |
| 作者姓名： | 刘俊娥 曾凡雷 郭章林 |
| 作者单位： | 1. 北京物资学院信息学院,北京,101149 2. 河北工程大学经济管理学院,河北邯郸,056038 3. 华北科技学院土木工程系,北京,101601 |
| 基金项目： | 国家“十一五”科技支持计划(2007BAB18B01); 北京市属高等学校人才强教计划资助项目(PHR200906210) |
| 摘 要： | 为挖掘瓦斯突出风险与煤矿开采中各影响因素间的关系,应用支持向量机(SVM)理论从模式判别角度分析瓦斯突出风险与各地质因素组成的特征向量间的判别关系,基于粗糙集(RS)理论对待分析数据进行知识约简,提取核心判别指标,建立基于粗糙集-支持向量机(RS-SVM)的瓦斯突出风险判别模型。研究结果表明,RS知识约简方法可以很好地对原始数据中的冗余指标进行约简,通过对约简后指标数据进行SVM回归分析,可对煤与瓦斯突出模式进行很好的判别,所建立的瓦斯突出风险判别模型较一般SVM模型具有更高的预测精度,同时指标约简过程降低SVM运算中的复杂度,提高运算效率。 |
| 关 键 词： | 煤与瓦斯突出 粗糙集(RS)理论 支持向量机(SVM) 风险评价 指标约简 |
Study on Multiple Factors Risk Evaluation of Coal and Gas Outburst Based on RS-SVM Model |
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| LIU Jun-e,ZENG Fan-lei,GUO Zhang-lin.Study on Multiple Factors Risk Evaluation of Coal and Gas Outburst Based on RS-SVM Model[J].China Safety Science Journal,2011,21(7). | |
| Authors: | LIU Jun-e ZENG Fan-lei GUO Zhang-lin |
| Institution: | LIU Jun-e1 ZENG Fan-lei2 GUO Zhang-lin3(1 School of Information,Beijing Wuzi University,Beijing 101149,China 2 School of Economics and Management,Hebei Engineering University,Handan Hebei 056038,China 3 Department of Civil Engineering,North China Institute of Science and Technology,Beijing 101601,China) |
| Abstract: | In order to study the relationship between gas outburst risk and its affecting factors in coal mine,with the application of SVM theory,the judgment relation between gas outburst risk and the eigenvectors consisted of geological factors is analyzed from the perspective of pattern recognition,then RS model is used to do the knowledge reduction about the original data and to extract the key indexes,so as to establish a risk determination model based on RS-SVM theory.Results show that redundancy of the indicato... |
| Keywords: | coal and gas outburst rough set(RS) theory support vector machine(SVM) risk evaluation index reduction |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |