基于投影寻踪回归的铜闪速熔炼过程关键工艺指标预测
来源期刊：中国有色金属学报2012年第11期
论文作者：刘建华 桂卫华 谢永芳 王雅琳 蒋朝辉
文章页码：3255 - 3260
关键词：铜闪速熔炼过程；投影寻踪回归；相似性度量；加速遗传算法
Key words：copper flash smelting process; projection pursuit regression; similarity measure; acceleration genetic algorithm
摘 要：针对数据量庞大引起模型参数更新时样本选择困难及训练速度慢的缺陷, 提出基于投影寻踪回归的铜闪速熔炼过程关键工艺指标预测方法。首先采用机器学习方式提取用于建模所需的相似样本集，借助投影寻踪回归思想, 建立铜闪速熔炼过程关键工艺指标预测模型；然后利用基于实数编码的加速遗传算法进行模型参数的实时更新。训练样本的机器选择可以避免人工选择带来的主观性和盲目性缺陷, 模型参数的更新训练只在相似样本集中进行，可有效提高模型参数更新速度。实际生产数据仿真结果验证了所提方法的有效性和可行性。
Abstract: Aimed at the shortcoming of difficult in choosing training samples and the slow training speed, which are caused by enormous data, a method based on projection pursuit regression that can predict the key process indicators for copper flash smelting process was proposed. With the similar samples set retrieved from the data base, the model of predicting key process indictors was developed by using projection pursuit regression method, and the model parameters were updated with acceleration algorithm in time, which is useful in avoiding the defects of subjectivity and blindness caused by artificial factors to select training samples. As the model parameters update training is only carried out within the similar samples, the update speed of the model parameters is effectively improved. Simulation results of actual data from a copper flash smelting process are given to verify the effectiveness and feasibility of the proposed method.
刘建华，桂卫华，谢永芳，王雅琳，蒋朝辉
(中南大学 信息科学与工程学院，长沙 410083)
摘 要：针对数据量庞大引起模型参数更新时样本选择困难及训练速度慢的缺陷, 提出基于投影寻踪回归的铜闪速熔炼过程关键工艺指标预测方法。首先采用机器学习方式提取用于建模所需的相似样本集，借助投影寻踪回归思想, 建立铜闪速熔炼过程关键工艺指标预测模型；然后利用基于实数编码的加速遗传算法进行模型参数的实时更新。训练样本的机器选择可以避免人工选择带来的主观性和盲目性缺陷, 模型参数的更新训练只在相似样本集中进行，可有效提高模型参数更新速度。实际生产数据仿真结果验证了所提方法的有效性和可行性。
关键词：铜闪速熔炼过程；投影寻踪回归；相似性度量；加速遗传算法
LIU Jian-hua, GUI Wei-hua, XIE Yong-fang, WANG Ya-lin, JIANG Zhao-hui
(School of Information Science and Engineering, Central South University, Changsha 410083, China)
Abstract:Aimed at the shortcoming of difficult in choosing training samples and the slow training speed, which are caused by enormous data, a method based on projection pursuit regression that can predict the key process indicators for copper flash smelting process was proposed. With the similar samples set retrieved from the data base, the model of predicting key process indictors was developed by using projection pursuit regression method, and the model parameters were updated with acceleration algorithm in time, which is useful in avoiding the defects of subjectivity and blindness caused by artificial factors to select training samples. As the model parameters update training is only carried out within the similar samples, the update speed of the model parameters is effectively improved. Simulation results of actual data from a copper flash smelting process are given to verify the effectiveness and feasibility of the proposed method.
Key words:copper flash smelting process; projection pursuit regression; similarity measure; acceleration genetic algorithm
