基于粗糙集融合最小二乘支持向量机的煤矿安全预警模型
投稿时间:2020-03-21  修订日期:2020-03-21  点此下载全文
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作者单位E-mail
陈常晖 福建船政交通职业学院信息工程系 chenchanghui2020@163.com 
基金项目:福建省社科联福建省社会科学规划项目“基于案例驱动的事故灾害应急方案生成方法研究”资助(编号:FJ2019B079)
中文摘要:针对煤矿安全影响因素多,各影响因素之间相互关联、样本信息采集困难等问题,分析了煤矿安全生产系统风险,从人-机-环-管四个方面构建了煤矿安全影响因素指标体系,基于粗糙集理论融合最小二乘支持向量机方法的提出了煤矿安全机预警模型,并以实测数据为例对该预警模型的计算结果进行了训练和检验。结果表明,粗糙集融合最小二乘支持向量机能够有效提高预警效率,反映各控制因素对煤矿安全的影响,计算结果与样本值拟合精度较高,对保障煤矿安全生产具有重要而现实的意义满足实际应用要求。
中文关键词:  煤矿安全预警  计算模型  最小二乘支持向量机  粗糙集  预警模型
 
Coal mine safety early warning model based on rough set fusion least squares support vector machine
Abstract:Aiming at the problems of many factors affecting coal mine safety, the correlation among various factors and the difficulty in collecting sample information, the risk of coal mine safety production system is analyzed. The index system of coal mine safety influence factors is constructed from four aspects. Based on rough set theory and least squares support vector machine (LS-SVM), an early warning model of coal mine safety machine is proposed. The calculation results of the model are trained and tested by taking the measured data. the results of the early warning model are trained and tested. The results show that the least squares support vector machine based on rough set fusion can effectively improve the efficiency of early warning and reflect the influence of various control factors on coal mine safety. The fitting accuracy between the calculated results and sample values is high, which has important and practical significance to ensure the safety production of coal minesmeets the requirements of practical application.
keywords:coal mine safety early warning  computing model  least square support vector machine  rough set  early warning model
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