采矿与安全工程学报2011，Vol.28Issue(2)：310-314,5.
基于加权LS-SVM时间序列短期瓦斯预测研究
Time Series Short-Term Gas Prediction Based on Weighted LS-SVM
摘要
Abstract
The neural network gas prediction model is poor in generalization performance and easy in falling into the local optimal value. In order to overcome these shortcomings, we propose the time series gas prediction method of least squares support vector machine (LS-SVM).However, in the LS-SVM case, the sparseness and robustness may lose, and the estimation of the support values is optimal only in the case of a Gaussian distribution of the error variables.So, this paper proposes the weighted LS-SVM to overcome these two drawbacks. Meanwhile,the optimal embedding dimension and delay time of time series are obtained by the smallest differential entropy method. On this basis, multi-step time series prediction algorithm steps are given based on the weighted LS-SVM. Finally, the data of gas outburst in working face of Hebi 10th mine is adopted to validate this model. The results show that the predict effect of shortterm the face gas emission is better using the weighted LS-SVM model than using LS-SVM by MATLAB 7.1.关键词
加权LS-SVM/时间序列/鲁棒性/瓦斯预测Key words
weighted LS-SVM/ time series/ robustness/ gas prediction分类
矿业与冶金引用本文复制引用
乔美英,马小平,兰建义,王莹..基于加权LS-SVM时间序列短期瓦斯预测研究[J].采矿与安全工程学报,2011,28(2):310-314,5.基金项目
国家自然科学基金项目(60974126) （60974126）
江苏省自然科学基金项目(BK2009094) （BK2009094）
