Drifting Modeling Method Using Weighted Support Vector Machines with Application to Soft Sensor
基于加权支持向量机的移动建模方法及其在软测量中的应用

The kernel problem in soft sensor of industrial processes is how to build the soft sensor model. However, there exist some questions to some extent in soft sensor model with convention-al modeling methods such as global single model and multiple models. Using the high generaliza-tion ability of support vector machines (SVMs) and the idea of locally weighted learning (LWL) algorithm, this paper proposes a novel learning algorithm named weighted support vector machines (W_ SVMs) which is suitable for local learning. We also present a drifting modeling method based on this algorithm. The proposed modeling method is applied to the estimation of Box-Jenkins gas furnace and FCCU and the simulation results show that the proposed approach is superior to the traditional modeling methods.