| PCA与ELM模型相结合的矿井突水水源快速识别方法研究 |
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| 引用本文： | 孙文洁,杨恒,李祥,王子超,杨蕾.PCA与ELM模型相结合的矿井突水水源快速识别方法研究[J].煤炭工程,2020,52(1):111-115. |
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| 作者姓名： | 孙文洁 杨恒 李祥 王子超 杨蕾 |
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| 作者单位： | 西安科技大学 |
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| 基金项目： | 国家重点研发计划;国家重点实验室基金;中国工程院重大咨询项目;国家自然科学基金;国家自然科学基金;国家重点实验室开放基金 |
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| 摘 要： | 为了快速准确判别矿井突水水源，降低矿井突水事故给煤矿生产及人类生命财产安全带来的危害，以赵各庄矿为例，提出了主成分分析法(PCA)与极限学习机(ELM)相结合矿井突水水源快速识别方法。结果表明：PCA确定了赵各庄矿中Na+、Ca2+、Mg2+对水样影响较大，为赵各庄矿水样的主控因子，排除了其它指标冗余信息的影响|在MATLAB中导入PCA确定的水样中三种主成分数据，通过ELM模型仿真训练可在10s内得出水样分类结果，分类学习时间迅速|对比ELM模型与BP神经网络对水样的分类结果，ELM仿真训练结果精确度高达100%，而BP神经网络仿真训练结果精确度仅为83.33%，远低于ELM模型精确度。
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| 关 键 词： | PCA模型 ELM模型 矿井突水 水源判别 赵各庄矿 |
| 收稿时间： | 2019-07-11 |
| 修稿时间： | 2019-08-19 |
Rapid identification method of mine water inrush source with coupled PCA and ELM model |
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| Abstract: | In recent years, mine water damage accidents have become more frequent. In order to reduce the harm caused by mine water inrush accident to coal mine production and human life and property safety, how to judge mine water Inrush source quickly and accurately is more and more important. Aiming at the above problems, a rapid identification method of mine water inrush source coupled with PCA-ELM model is proposed, and validates the method with the water source data of Zhaogezhuang mine as an example. Firstly, the principal component analysis method is used to reduce the dimension of the multi-dimensional variable data, and then the ELM algorithm is used to simulate the selected principal component. The results show that the method has stable classification performance and short classification learning time, which satisfies the condition of rapid identification of water inrush source. |
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