摘要
支持向量机(SVM)是一种基于结构风险最小化原理(SRM)的学习算法,也是一种具有很好的泛化性能的回归方法。针对青霉素发酵过程中的菌体浓度进行软测量建模,提出了一种新的基于距离的模糊支持向量机,并用序列最小优化算法(SMO)求解优化问题。仿真实例说明能够对青霉素发酵过程中不可在线测量的变量进行软测量,达到了较高的测量精度。
Support vector machine (SVM) is a learning algorithm based on structural risk minimization (SRM), it is a good regression way, which has good generalization ability also. A fuzzy support vector machine based on distance which used to model the mycelial concentration by soft sensor is proposed in this article. The optimization question is resolved by sequential minimal optimization (SMO). The simulation example shows that this FSVM could measure the parameters, which could not be measured online during the course of penicillin fermentation, with a high precision.
出处
《微计算机信息》
北大核心
2007年第19期300-302,共3页
Control & Automation
基金
国家"863"项目:功能基因亚功能片断的制备和质量控制技术平台(2002AA217131)
关键词
支持向量机
模糊
菌体浓度
距离
序列最小优化
support vector machine
fuzzy
mycelial concentration
distance
sequential minimal optimization
