摘要
提出一种基于最小二乘支持向量机的铁水含硅量软测量模型,采用遗传算法确定模型参数的优化组合。用某钢管厂高炉的实际生产数据经过预处理后作为模型的训练和测试样本,进行软测量实验。实验结果表明,与神经网络模型和时间序列分析模型比较,所提出的软测量模型的软测量精度更高。
A prediction model based on least square support vector machine （LSSVM） is developed to model and prediction hot metal silicon content in a blast furnace,the optimal set of the LSSVM model parameters is selected by using genetic algorithm. Estimation experiment is conducted based on the data obtained from a blast furnace past operation records in a steel tube plant and being preprocessed in several different＇ways.The experimental results show that the proposed meth- ods yielded more accurate predictions than neural network and time series analysis modeling methods.
出处
《工业控制计算机》
2013年第3期89-90,共2页
Industrial Control Computer
基金
湖南省科技厅科研资助项目(2011GK3188)
湖南省科技厅科研资助项目(2012FJ4332)
关键词
高炉
铁水含硅量
软测量
遗传算法
最小二乘支持向量机
blast furnace,hot metal silicon content,prediction,genetic algorithm,least squares support vector machine
