Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters Optimization

Abstract

In the design of multiple response parameters optimization, weighted principal component analysis (weighted PCA) is used to build the relationship between the response variables and controllable factor model by linear regression. But in the complicated nonlinear production process, the fit of the linear regression model is not high that cannot satisfy the requirement of the parameter design model. This study proposed an improved weighted PCA based on RBF neural network prediction model. In this paper, RBF neural network was used to construct nonlinear prediction model of production process and to adjust the weighted PCA algorithm by adding the predict ability index of neural network model. In the design of multiple response parameters, this approach improve the effect of process parameters optimization. And applied this method to multiple response parameters optimization design of metallization polypropylene film capacitor thermal polymerization process, the results show that capacitance value and the loss tangent are all improved, and the effect of optimization parameters is achieve to satisfactory results.