摘要
支持向量机算法用于软测量建模能较好地解决小样本、非线性、高维数、局部极值等问题.本文针对稀土萃取过程组分含量在线检测的难题,将具有径向基(RBF)核函数的支持向量机算法应用于稀土萃取过程组分含量软测量建模,并讨论了模型参数的选择及其对模型的影响.通过某稀土公司生产过程实际采集数据的仿真试验,结果表明基于支持向量机算法的组分含量软测量模型具有较高的泛化能力和较快的预测速度,是实现稀土萃取过程组分含量软测量的一种有效方法.
The problems of small sample, non-linearity,high dimensions and local minimal value can be well solved by supporting vector machine in soft-sensor modeling. In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, algorithm of SVM with RBF kennel is applied to the modeling of the rare earth extraction separation process. The selection and effect of model-parameters are discussed. Through the simu- lations of the model, it shows that the component content soft-sensor model based on SVM has both preferable generalization and high velocity. SVM is an effective method for rare earth extractingprocess soft-sensor.
出处
《华东交通大学学报》
2008年第1期123-126,共4页
Journal of East China Jiaotong University
基金
江西省教育厅项目(赣教技字[2006]183号
赣教技字[2007]185号)
华东交通大学科研基金项目(06ZKDQ02)
