改进在线最小二乘支持向量机的合金成分预测  

Prediction model for alloy composition based on improved online least squares support vector machine

作　　者：唐春霞[1,2] 阳春华[1] 桂卫华[1] 周志光[2] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]长沙民政学院电子信息工程系,长沙410004

出　　处：《计算机工程与应用》2011年第34期246-248,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60634020;No.60874069;No.60843002)~~

摘　　要：针对硅锰合金埋弧冶炼过程中合金成分检测困难、离线化验滞后大、难以实时控制的问题,提出一种改进在线最小二乘支持向量机(IOLSSVM)的合金成分预测模型。该模型对每一个新增样本采用增量式学习,根据样本对模型贡献的不同删除样本集中对模型贡献最小的样本数据,利用递推计算增强模型的在线学习能力。将此模型应用于30MVA硅锰合金埋弧炉冶炼过程合金成分预测,实际生产运行数据表明了此方法的有效性。To overcome the difficulty that the silicon-manganese alloy composition can not be effectively controlled in silico- manganese smelting process due to the lack of real-time on-line instrumentation,a prediction model based on Improved Online Least Squares Support Vector Machine（IOLSSVM） is proposed.According to the contribution of each data point to the prediction model,the least important data point is removed from the data set when adding new ones.The online learning ability is also improved by using the recursive algorithm.This method is applied to predict the silicon-manganese composition in a 30MVA submerged arc furnace smelting process and the results show its effectiveness.

关 键 词：硅锰合金 埋弧炉 改进的在线最小二乘支持向量机 合金成分 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]