摘要
针对所有样本点均出现在最小二乘支持向量机模型中的缺陷,提出了一种改进的最小二乘支持向量机回归方法。根据样本点间欧氏距离的大小,去除原变量空间中大部分的样本点,从而获得回归模型的“稀疏”特性,大大简化模型复杂程度。同时,将这一方法应用于生物发酵过程,建立青霉素发酵过程中产物浓度的软测量模型,实现青霉素浓度的在线预估。实验研究结果表明,所提方法为生物发酵过程中难于在线测量质量参数的实时监测提供了一个有效的手段。
A regression method is proposed to improve the LS-SVM （Least Square-Support Vector Machine） model. The sparseness of LS-SVM is thus obtained from the regression model to decrease greatly the computation quantity by way of removing most of the original sample points in accordance with their Euclidian distances. The proposed method has been applied to the fermentation process. A soft sensor model to develop a soft sensor so as to estimate the product＇s concentration on line in penicillin fermentation process. Testing results showed that the proposed procedure can provide a new useful approach to the real time monitoring of quality variables which are hard to be measured on-line in fermentation processes.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2006年第3期241-244,271,共5页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(60374003)
973计划子课题(2002CB312200)
教育部暨辽宁省流程工业综合自动化重点实验室开放课题基金(PAL200511)资助项目
关键词
软测量
最小二乘支持向量机
生物发酵
青霉素浓度
Soft sensing Least square-support vector machine Fermentation process Penicillin concentration
