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(英文)

Drifting Modeling Method Using Weighted Support Vector Machines with Application to Soft Sensor

工业过程软测量技术的核心问题是建立软测量模型,然而,利用传统全局建模方法与多模型建模方法进行复杂工业过程软测量建模时,在不同程度上存在一些问题.本文利用支持向量机(SVMs)泛化能力强的特点,结合局部加权学习(LWL)算法思想,提出一种适于局部学习的加权支持向量机(W-SVMs)学习算法和基于这种算法的移动建模方法.利用这种建模方法对Box-Jenkins煤气炉和重油催化裂化(FCCU)装置进行分析建模,并与其它不同建模方法进行比较,显示了该方法的优点和有效性.

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 conventional modeling methods such as global single model and multiple models. Using the high generalization 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.