中文摘要：

多模型途径能显著地与多重运作的条件在这进程改进软传感器的预言性能。然而，传统的聚类算法可以在子类导致重叠现象，以便边班和孤立点不能有效地被处理，当模特儿的结果不是令人满意的。以便解决这些问题，基于加权的内核菲希尔，标准被介绍改进聚类的精确性，在特征，印射被采用更近把边类和孤立点带到另外的正常子类的一个新特征抽取方法。而且，分类数据被用来基于支持向量机器开发一个多重模型。建议方法被用于 bisphenol 为优秀索引的预言的一个生产过程。模拟结果在改进数据分类和软传感器的预言表演表明它的能力。

英文摘要：

Multi-model approach can significantly improve the prediction performance of soft sensors in the proc- ess with multiple operational conditions. However, traditional clustering algorithms may result in overlapping phe- nomenon in subclasses, so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory. In order to solve these problems, a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy, in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses. Furthermore, the classified data are used to develop a multiple model based on support vector machine. The proposed method is applied to a bisphenol A production process for prediction of the quality index. The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.