Multi-Response Parameters Optimization Based on PCA and Neural Network

Authors

Jianli Yu, 1.School of Management Science Engineering, Zhengzhou University of Aeronautical, Zhengzhou 450046, China; ;
Hongqi Huang, 1.School of Management Science Engineering, Zhengzhou University of Aeronautical, Zhengzhou 450046, China; ;
Manxiang Miao, 2.School of Mechatronics Engineering, Zhengzhou University of Aeronautical, Zhengzhou 450046, China;

Abstract

Abstract: A multi-response parameters optimization method based on principal component analysis (PCA) and neural network is proposed. It is used to optimize temperature and time parameters in complex thermal polymerization process. By using the method of weighted PCA, two response indexes, capacity value and loss tangent value, are converted into a single quality performance index. The main effect value is used to identify the search range. The radical basis function (RBF) neural network model is established to search and identify the optimal process parameters. Results show that response indexes are improved and the optimization effect is obvious. Therefore, this study method is a convenient and effective method to solve the complicated nonlinear response process parameters optimization, and has practical application value.

Recommended Citation

Yu, Jianli; Huang, Hongqi; and Miao, Manxiang (2019) "Multi-Response Parameters Optimization Based on PCA and Neural Network," Journal of System Simulation: Vol. 30: Iss. 1, Article 22.
DOI: 10.16182/j.issn1004731x.joss.201801022
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss1/22

First Page

176

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201801022

Last Page

184

CLC

TP114.2

Recommended Citation

Yu Jianli, Huang Hongqi, Miao Manxiang. Multi-Response Parameters Optimization Based on PCA and Neural Network[J]. Journal of System Simulation, 2018, 30(1): 176-184.

DOI

10.16182/j.issn1004731x.joss.201801022