摘要
研究了基于最小二乘支持向量机的软测量建模方法,并将其应用于造纸企业碱回收蒸发工段黑液浓度的预测。应用结果表明,该建模方法的可行性和有效性完全能满足工业生产的需要。
Support vector machine （SVM） is a novel machine learning method, which is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima, and has both high universality and good extendibility. In this paper, soft measurement modeling method based on Least Square SVM （IS-SVM） is proposed and is applied to the predication of black liquor concentration at evaporation section of alkali recovery plant. The practice shows that this modeling method based on IS-SVM is effective and feasible and can satisfy the needs of industrial production.
出处
《中国造纸》
CAS
北大核心
2005年第12期22-24,共3页
China Pulp & Paper
基金
四川省教育厅青年基金资助项目和宜宾学院重点研究项目(2004B024)
关键词
软测量
支持向量机
最小二乘支持向量机
soft measurement
support vector machine
least squares support vector machine
