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| 基于加权LS-SVM时间序列短期瓦斯预测研究 | |
| 引用本文： | 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 |
| 作者姓名： | 乔美英 马小平 兰建义 王莹 |
| 作者单位： | 1. 中国矿业大学信息与电气工程学院,江苏,徐州,221116;河南理工大学电气工程与自动化学院,河南,焦作,454000 2. 中国矿业大学信息与电气工程学院,江苏,徐州,221116 3. 河南理工大学能源科学与工程学院,河南,焦作,454000 |
| 基金项目： | 国家自然科学基金项目(60974126); 江苏省自然科学基金项目(BK2009094) |
| 摘 要： | 针对神经网络的瓦斯预测模型存在的泛化性能差且存在易陷入局部最优的缺点,提出了基于最小二乘支持向量机(LS-SVM)时间序列瓦斯预测方法.由于标准最小二乘支持向量机(LS-SVM)要求样本误差分布服从高斯分布,且标准LS-SVM丧失鲁棒性与稀疏性等特点,提出了基于加权LS-SVM的瓦斯时间序列预测的方法,从而提高了标准L... |
| 关 键 词： | 加权LS-SVM 时间序列 鲁棒性 瓦斯预测 |
Time Series Short-Term Gas Prediction Based on Weighted LS-SVM |
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| QIAO Mei-ying,MA Xiao-ping,LAN Jian-yi,WANG Ying. Time Series Short-Term Gas Prediction Based on Weighted LS-SVM[J]. Journal of Mining and Safety Engineering, 2011, 28(2): 310-314 | |
| Authors: | QIAO Mei-ying MA Xiao-ping LAN Jian-yi WANG Ying |
| Affiliation: | QIAO Mei-ying1,2,MA Xiao-ping1,LAN Jian-yi3,WANG Ying1(1.School of Information and Electrical Engineering,China University Mining & Technology,Xuzhou,Jiangsu 221116,China,2.School of Electrical Engineering and Automation,HenanPolytechnic University,Jiaozuo,Henan 454000,3.School of Energy Science andEngineering,Henan Polytechnic University,China) |
| 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 tw... |
| Keywords: | weighted LS-SVM time series robustness gas prediction |
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