Research on Soft Sensor Modeling of Fermentation Process Based on v-SVR

Support vector regression (SVR) is a novel type of learning machine, which has shown to provide better generalization performance than traditional techniques. This thesis introduces a new type of support vector machine for regression (v-SVR), which based on SVR. The new algorithm can control the accuracy of fitness and prediction error by adjusting the parameter v. In the experiments v-SVR is used for soft sensor modeling of fermentation process. The results show that v- SVR has low error rate and better generalization with appropriate v.