摘要
针对常规v支持向量回归用于在线建模时存在的问题,提出了一种支持向量回归在线建模方法.利用贝叶斯证据框架优化模型参数,通过判断新增观测值是否满足原来的KKT条件,并对历史数据给予不同程度的加权以充分利用最新的数据信息,使模型随着时间的推移在线更新.工业PTA氧化过程中4-CBA含量预测的实例表明,该方法能很好地跟踪4-CBA含量的变化趋势,是一种有效的在线建模方法.*
In order solve the problems in the application of v support vector regression （v-SVR） to on-line modeling, a support vector regression on-line modeling method is proposed. Bayesian evidence framework is used to optimize the model parameters. Through determining whether the new observation satisfies the original KKT conditions and assigning different weighting factors to the historical data, the latest data can be used sufficiently, and the model can be refreshed on-line as time passes by. The proposed approach is successfully applied to predict the concentration of 4-carboxybenzaldhyde （4-CBA） in industrial purified terephthalic acid （PTA） oxidation process. The results indicate that the proposed method can track the trend of 4-CBA and it is an effective method for on-line modeling.
出处
《信息与控制》
CSCD
北大核心
2005年第5期636-640,共5页
Information and Control
基金
国家973计划资助项目(2002CB3122000)
国家自然科学基金资助项目(60074027)
国家863计划资助项目(2003AA412010)
关键词
支持向量机
回归
在线建模
4-CBA
support vector machine（SVM）
regression
on-line modeling
4-CBA
