关键词: 线性判别分析组合支持向量机分类FONT-SIZE: 9pt, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>;FONT-SIZE: 9pt, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA;

Abstract:

When a soft sensor model is constructed for a complicated production process,a single support vector machine(SVM)model or a compositional SVM model based on conventional clustering methods sometimes cannot track mutant signal well or obtain a satisfactory generalization.An improved linear discriminant analysis(LDA)algorithm is proposed in this paper so as to solve the problem.The feature vectors are obtained by combining boundary analysis with LDA between the categories.The original sample data are transformed in terms of the feature vectors,and sub-models based on SVM are respectively constructed by transformed data.And then the compositional parameters for sub-models are designed according to the sum of the effectual characteristic values in the every feature vector.Finally,a compositional SVM model is constructed.The simulation results show that the composition model can reduce the information interference among the different data categories and improve the inferential accuracy of the model.

Key words: 线性判别分析组合支持向量机分类FONT-SIZE: 9pt, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>;FONT-SIZE: 9pt, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA;