中文摘要：

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.

英文摘要：

It is difficult for a single model to express the complicated production process, and it often re- sults 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.