摘要
提出了一种基于支持向量机的软测量方法 ,并建立了青霉素发酵过程中菌丝浓度的软测量模型 ,通过实验分析了参数调整和核函数选择对支持向量机建模的影响 .利用现场数据建立各种软测量模型可以发现 ,与其他软测量方法相比 ,支持向量机方法在理论上优于人工神经网络等其他建模方法 .
A soft sensor method based on support vector machine is presented, and a soft sensor model for biomass estimation in penicillium fermentation process is developed by this method.The effects of parameter adjusting and selection of kernel functions on model quality are analyzed by simulation experiments.With data collected from real plant,models based on some other technologies are also presented.Experiments show that support vector machine method is superior to traditional modeling methods in theory.
出处
《信息与控制》
CSCD
北大核心
2004年第4期417-421,共5页
Information and Control
关键词
支持向量机
发酵
软测量
神经网络
部分最tb-乘
support vector machine
fermentation
soft-sensor
neural networks
partial least squares
