基于高斯过程和贝叶斯决策的组合模型软测量

为了提高化工生产过程中软测量建模的估计精度，提出了一种基于高斯过程和贝叶斯决策的组合模型建模方法。该方法在对原始数据进行分类的基础上，利用高斯过程对每个子类建立软测量子模型，通过贝叶斯决策方法实现模型的联合估计输出。将该建模方法应用于某双酚A装置的软测量建模中，仿真结果表明，相比于传统的开关切换或加权组合多模型，该组合模型能在实际生产中充分利用样本信息，使得具有更高的估计精度和更强的泛化性能。

Combination model soft sensor based on Gaussian process and Bayesian committee machine

In order to improve the estimation accuracy of a soft sensor in the process of chemical production,a combination model for soft sensor is presented based on Gaussian process and Bayesian committee machine.The original data are classified into several subclasses,and then,the sub-models are built by Gaussian process regression.In order to get a global probabilistic prediction,Bayesian committee machine is used to combine the outputs of the sub-estimators.Finally,the algorithm is applied to a soft sensor model for a production plant of bisphenol A.Simulation results show that the integration algorithm can make full use of sample information in the actual production,and the estimated accuracy of model is improved,and the generalization ability is better,comparing to the traditional switch or a weighted combination of multiple model.