| 引用本文: | 唐贤伦,庄陵,胡向东.铁水硅含量的混沌粒子群支持向量机预报方法[J].控制理论与应用,2009,26(8):838~842.[点击复制] |
| TANG Xian-lun,ZHUANG Ling,HU Xiangdong.The support vector regression based on the chaos particle swarm optimization algorithm for the prediction of silicon content in hot metal[J].Control Theory and Technology,2009,26(8):838~842.[点击复制] |
|
| 铁水硅含量的混沌粒子群支持向量机预报方法 |
| The support vector regression based on the chaos particle swarm optimization algorithm for the prediction of silicon content in hot metal |
| 摘要点击 2206 全文点击 1978 投稿时间：2008-08-07 修订日期：2008-12-13 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/j.issn.1000-8152.2009.8.CCTA080836 |
| 2009,26(8):838-842 |
| 中文关键词 支持向量机 粒子群优化 参数优化 预测 铁水硅含量 |
| 英文关键词 support vector regression particle swarm optimization parameters optimization prediction silicon content in hot metal |
| 基金项目 国家自然科学基金资助项目(60506055); 重庆邮电大学科研基金资助项目(A2008–5). |
|
| 中文摘要 |
| 提出一种基于混沌粒子群优化(CPSO)的支持向量回归机(SVR)参数优化算法, 并使用该算法建立高炉铁水硅含量预测模型(CPSO–SVR), 对某大型钢铁厂高炉铁水硅含量的实际采集数据进行预测, 结果表明基于混沌粒子群优化算法寻优的参数建立的铁水硅含量支持向量回归预测模型具有良好的预测效果. 与最小二乘支持向量回归机(LS–SVR)、使用粒子群优化算法训练的神经网络(PSO–NN)进行比较, CPSO–SVR模型对铁水硅含量进行预测时预测绝对误差小于0.03的样本数占总测试样本数的百分比达到90%以上, 预测效果明显优于PSO–NN, 且比LS–SVR稳定性更强, 可用于高炉铁水硅含量的实际预测, 表明混沌粒子群优化算法是选取SVR参数的有效方法. |
| 英文摘要 |
| An optimal selection approach of support vector regression parameters is proposed based on the chaos particle swarm optimization(CPSO) algorithm; A model based on the support vector regression to predict the silicon content in hot metal is established; and the optimal parameters of which is searched by CPSO. The data of the model are also collected from the No.3 BF in Panzhihua Iron and Steel Group Co. The results show that the proposed prediction model has better prediction results than the neural network trained by particle swarm optimization and least squares support vector regression algorithm; the percentage of samples with absolute prediction error less than 0.03 is higher than 90%, when predicting the silicon content by the proposed model. This indicates that the prediction precision can meet the requirement of practical production and demonstrates that the CPSO is an effective approach for parameter optimization of support-vector regression. |