A Combination Model Based on Feature Extraction of the Weighted Kernel Fisher Criterion
In this paper, we present a weighted kernel Fisher criterion based on the feature extraction to improve the classification accuracy. The basic idea of the weighted kernel Fisher criterion is to bring the edged classes and points closer to the normal sample classes. The motivation of the work is to solve the problems on subclasses which may be overlapped when using the traditional clustering algorithm. The proposed method is applied to soft sensor modeling for the quality index in Bisphenol A production process. Numerical examples as well as an experiment are employed to demonstrate the effectiveness of the proposed method.
feature extraction weighted kernel Fisher criterion classification soft sensor modeling
Lv Ye Dong Tao Yang Huizhong
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) of Jiangnan Un Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) of Jiangnan Un
国内会议
厦门
英文
1-6
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）