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基于最小二乘支持向量机的煤矿瓦斯预测
引用本文:史庆军,戚常林,杨松涛,张春玲.基于最小二乘支持向量机的煤矿瓦斯预测[J].佳木斯大学学报,2011,29(1):47-49.
作者姓名:史庆军  戚常林  杨松涛  张春玲
作者单位:佳木斯大学信息电子技术学院;
基金项目:黑龙江省教育厅科研项目(11511408); 佳木斯大学校级重点项目(Lz2010-013)
摘    要:瓦斯涌出量受多种自然因素和开发技术的影响,是一个非线性、高维的问题.提出了改进的PSO算法与LSSVM算法相结合对瓦斯涌出量进行预测的新方法.实验结果表明,该模型预测精度更高,泛化能力更强.

关 键 词:瓦斯涌出量  最小二乘支持向量机  粒子群算法

Prediction of Gas Emission Based on Least Squares Support Vector Machines
SHI Qing-jun,QI Chang-lin,YANG Shong-tao,ZHANG Chun-ling.Prediction of Gas Emission Based on Least Squares Support Vector Machines[J].Journal of Jiamusi University(Natural Science Edition),2011,29(1):47-49.
Authors:SHI Qing-jun  QI Chang-lin  YANG Shong-tao  ZHANG Chun-ling
Institution:SHI Qing-jun,QI Chang-lin,YANG Shong-tao,ZHANG Chun-ling(College of Information and Electronic Technology,Jiamusi University,Jiamusi 154007,China)
Abstract:Gas emission is influenced by series natural factors and exploiting conditions,which is a non-linearly and high-dimension problem.A new method of combining the improved PSO with LSSVM is presented to predict gas emission.Experimental results show that the model can achieve more accurate prediction and stronger generative ability.
Keywords:gas emission  LSSVM  PSO  
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