KPCA_RVM
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关于关联向量机应用的最新文献！ 提出了一种核主元分析(KPCA)和关联向量机(RVM)相结合的组合建模方法。KPCA-RVM采用KPCA对原始自变量进行非线性变换并提取主成分,形成特征自变量 采用RVM,对KPCA变换后的样本数据进行回归建模,并根据模型的预报能力自适应的确定参与回归的最佳特征变量个数,消除冗余信息干扰,获得强非线性表达能力且预报性能良好的模型。并将KPCA-RVM应用于PTA装置对羧基苯甲醛(4-CBA)含量的软测量建模,结果表明该方法预测精度高于PCA-RVM和RVM。(A novel modeling method integrated KPCA with RVM was proposed. The kernel primary component analysis (KPCA) was employed to identify the principal components from the nonlinear transform data of independent variables, which were regarded as character variables. Regression between character variables and dependent variables was done based on RVM, and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model. Thus, KPCA-RVM meth...)
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KPCA_RVM.pdf
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