一种基于自适应模糊高斯核聚类的软测量建模方法

单一模型一般难以表达复杂的生产过程特性,在软测量应用中往往容易使模型的估计精度低、泛化性能差.提出一种基于自适应模糊高斯核聚类的概率加权多模型融合方法,利用高维空间内样本的分散性来确定聚类中心,能取得最佳聚类效果.根据贝叶斯后验定律进行多模型融合,使总模型输出更具合理性.该方法不仅克服了单模型预测的局限性,同时对传统多模型融合方法做了一些改进,提高了过程估计的精度.

A Soft Sensor Modeling Method Based on Self Adaptive Fuzzy Gauss Kernel Clustering

It is difficult for a single model to express the complicated production process, and it often results in low accuracy of prediction and poor performance of generalization. This paper presents a multi model fusion method based on probability weight and self adaptive fuzzy Gauss kernel clustering. The method determines cluster centers according to dispersion of the samples in a high dimensional space. The weight of every sub model is given by Bayesian posterior method. The method can overcome the limitation of single model forecast and improve traditional multi model fusion methods for obtaining higher prediction accuracy.