摘要
建立了基于支持向量机(Support Vector Machine,SVM)的煤自燃极限参数预测模型;经过与多项式函数及Sigmoid核函数的对比,选用径向基函数作为SVM核函数;提出了一种SVM参数优化的变步长搜索方法,先在一个大区域根据训练样本均方差的值改变参数搜索步长,找到一个性能好的小区域,在这个小区域中应用网格搜索法找到最优参数,可提高参数搜索速度.实验表明,与人工神经网络模型相比,在样本有限的情况下,基于支持向量机的煤自燃极限参数预测模型预测精度更高、速度更快,说明支持向量机技术在煤自燃极限参数预测中具有实用价值.
A SVM （Support Vector Machine） model for predicting the limit parameters of coal self-ignition was developed. By comparing with polynomial function and Sigmoid function, radial basic function was selected as the kernel function of SVM. A method for optimizing SVM parameters was proposed. The procedure of this method includes three stages： （1） Selecting an area large enough to cover the optimal parameters; （2） Searching the large area with search step length changing along with the mean squared error of training set, to quickly locate a small area in which the optimal parameters lie; （3） Searching the small area grid by grid for the optimal parameter, which is obviously more efficient than searching the large area directly with grid search method. Experimental results show that, when used for predicting limit parameters of coal self-ignition, SVM-based model performed significantly better than the neural network-based model on both prediction-precision and modeling speed, especially under the condition of limited training samples. So, SVM is feasible for predicting the limit parameters of coal self-ignition.
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
2009年第11期1489-1493,共5页
Journal of China Coal Society
基金
国家自然科学基金资助项目(50376070)
江苏省高校自然科学基金资助项目(08KJD520022)
关键词
煤自燃极限参数
支持向量机
人工神经网络
预测模型
limit parameters of coal self-ignition
support vector machine
artificial neural network
predicting model
