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

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 li...

Full description

Bibliographic Details
Main Authors: Yu Jianli, Pan Xiaotian, Huang Hongqi
Format: Article
Language:English
Published: EDP Sciences 2017-01-01
Series:MATEC Web of Conferences
Online Access:https://doi.org/10.1051/matecconf/201710002039
id doaj-ae952a8f38f649eaaeecf88abdb57a73
record_format Article
spelling doaj-ae952a8f38f649eaaeecf88abdb57a732021-02-02T00:36:26ZengEDP SciencesMATEC Web of Conferences2261-236X2017-01-011000203910.1051/matecconf/201710002039matecconf_gcmm2017_02039Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters OptimizationYu JianliPan XiaotianHuang HongqiIn 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.https://doi.org/10.1051/matecconf/201710002039
collection DOAJ
language English
format Article
sources DOAJ
author Yu Jianli
Pan Xiaotian
Huang Hongqi
spellingShingle Yu Jianli
Pan Xiaotian
Huang Hongqi
Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters Optimization
MATEC Web of Conferences
author_facet Yu Jianli
Pan Xiaotian
Huang Hongqi
author_sort Yu Jianli
title Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters Optimization
title_short Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters Optimization
title_full Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters Optimization
title_fullStr Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters Optimization
title_full_unstemmed Improved PCA Method Based on RBF Neural Network for Multiple Response Parameters Optimization
title_sort improved pca method based on rbf neural network for multiple response parameters optimization
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2017-01-01
description 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.
url https://doi.org/10.1051/matecconf/201710002039
work_keys_str_mv AT yujianli improvedpcamethodbasedonrbfneuralnetworkformultipleresponseparametersoptimization
AT panxiaotian improvedpcamethodbasedonrbfneuralnetworkformultipleresponseparametersoptimization
AT huanghongqi improvedpcamethodbasedonrbfneuralnetworkformultipleresponseparametersoptimization
_version_ 1724313529120456704