| 基于蚁群算法优化SVM的瓦斯涌出量预测 |
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| 引用本文： | 王晓路.基于蚁群算法优化SVM的瓦斯涌出量预测[J].煤炭技术,2011,30(5). |
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| 作者姓名： | 王晓路 |
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| 作者单位： | 西安科技大学通信与信息工程学院,西安,710054 |
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| 基金项目： | 电子信息产业发展基金招标项目，国家高技术研究发展计划(863计划)，陕西省教育厅项目 |
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| 摘 要： | 为了提高瓦斯涌出量预测的精度和预测模型的泛化能力,提出了一种基于蚁群算法(ACO)优化支持向量机(SVM)参数的瓦斯涌出量预测方法。在SVM所建立预测模型中各个参数的取值区间内,采用蚁群优化算法计算预测模型各个参数的最佳值,基于最佳参数的SVM建立瓦斯涌出量预测模型。结果表明:采用未优化的SVM建立的预测方法,其个别预测误差相对较大,最大误差为8.11%,平均误差为4.68%,采用ACO对于预测模型的参数进行优化后,预测性能有显著提高,最大误差为4.37%,平均误差为2.89%,表明所建议的方法是有效、可行的。
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| 关 键 词： | 瓦斯涌出量 蚁群优化算法 支持向量机 预测 |
Forecasting of Gas Emission Quantity Based on Ant Colony Optimization SVM |
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| WANG Xiao-lu.Forecasting of Gas Emission Quantity Based on Ant Colony Optimization SVM[J].Coal Technology,2011,30(5). |
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| Authors: | WANG Xiao-lu |
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| Affiliation: | WANG Xiao-lu(School of Communication and Information Engineering,Xi'an University of Science & Technology,Xi'an 710054,China) |
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| Abstract: | To accurately predict the gas emission quantity and improve forecasting performance,an approach based on the parameters of Support Vector Machine(SVM) being optimized by Ant Colony Optimization(ACO) arithmetic is proposed.ACO is introduced to calculate the best parameters in their respective value interval of SVM forecasting model.The forecasting model of gas emission quantity is established based on the SVM with the best parameters.The results show that some prediction deviations are relatively large with ... |
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| Keywords: | gas emission ant volony optimization support vector machine forecasting |
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