Prediction method of coal spontaneous combustion based on relevance vector machine
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摘要: 在煤自燃程度预测方面，基于径向基（RBF）神经网络的方法结构复杂、易陷入局部最优，基于支持向量机（SVM）方法的核函数受Mercer条件限制而对参数敏感，传统的机器学习方法误差较大。针对上述问题，提出了一种基于相关向量机（RVM）的煤自燃预测方法。以易发生煤自燃现象的亭南煤矿为例，模拟煤样自燃升温过程并采集气体浓度与煤自燃温度数据，建立训练样本和测试样本；由训练样本构建RVM模型，得到模型的最优参数；将测试样本代入已训练的RVM模型中，预测煤自燃温度值。与基于RBF神经网络和SVM的煤自燃预测方法进行比较，结果表明，基于RBF神经网络和SVM的煤自燃预测方法训练误差较小，但测试误差较大，说明这2种方法存在过拟合现象，泛化能力差；基于RVM的煤自燃预测方法的训练误差与测试误差比较接近且预测精度最高。Abstract: In terms of coal spontaneous combustion degree prediction, the radial basis function (RBF) neural network method is complex in structure and easy to fall into local optimum, the kernel function based on support vector machine (SVM) is sensitive to parameters due to Mercer condition, the traditional machine learning method has a large error. For the above problems, a coal spontaneous combustion prediction method based on relevance vector machine (RVM) is proposed. Taking Tingnan Coal Mine which is prone to spontaneous combustion as an example, the temperature rising process of coal sample spontaneous combustion is simulated, and the data of gas concentration and coal spontaneous combustion temperature are collected to establish training samples and test samples. The RVM model is constructed from the training samples, and the optimal parameters of the model are obtained. The test samples are substituted into the trained RVM model to predict coal spontaneous combustion temperature. Compared with coal spontaneous combustion prediction methods based on RBF neural network and SVM, the results show that the coal spontaneous combustion prediction methods based on RBF neural network and SVM have small training error but large test error, which indicates that the two methods have over fitting phenomenon and poor generalization ability. The training error and test error of the coal spontaneous combustion prediction method based on RVM are close and prediction accuracy is the highest.
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