支持向量機（SVM）是一種基于結構風險最小化原理(SRM)的學習算法，也是一種具有很好的泛化性能的回歸方法。針對青霉素發酵過程中的菌體濃度進行軟測量建模，提出了一種新的基于距離的模糊支持向量機，并用序列最小優化算法(SMO)求解優化問題。仿真實例說明能夠對青霉素發酵過程中不可在線測量的變量進行軟測量，達到了較高的測量精度。
關鍵詞: 支持向量機；模糊；菌體濃度；距離；序列最小優化；

Application of Fuzzy Support Machine in Penicillin Ferment Zhenya Yu, Wenxia Wang, Feng Pan （School of Communication and Control Engineering, Southern Yangtze University, Wuxi 214122） Abstract: 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.
Keyword: support vector machine; fuzzy; mycelial concentration; distance; sequential minimal
optimization