Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

介绍是一个多重模型基于亲密关系，繁殖(AP ) ， Gaussian 过程(GP ) 和贝叶斯的委员会用机器制造的软察觉到方法(BCM ) 。聚类算术的 AP 被用来根据他们的操作的点聚类训练样品。然后，亚模型被 Gaussian 过程回归(GPR ) 估计。最后，以便得到全球概率的预言，贝叶斯的委员会机器被用来联合亚评估者的产量。建议方法被使用了在氢化裂解器分馏器预言轻石油结束点。实际应用显示它为在化学过程监视的质量的联机预言是有用的。

Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.