基于主成分分析与神经网络的多响应参数优化

doi:10.16182/j.issn1004731x.joss.201801022

摘要/Abstract

摘要： 研究多响应参数优化问题,给出一种基于主成分分析与神经网络的参数优化方法,对复杂热聚合工艺中温度和时间参数进行优化设计。用加权主成分分析方法将容值和损耗正切值两个响应质量指标转化为单一的质量绩效指标,用其主效应值确定优化范围;建立径向基神经网络模型,搜索并确定最优工艺参数。结果表明,该方法设计的最优工艺参数使两个响应指标均得到较大改善,优化效果明显,是解决复杂非线性多响应工艺参数优化的一种方便有效的方法,具有实际应用价值。

关键词: 主成分分析, 神经网络, 金属化膜电容器, 参数优化

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.

Key words: PCA, neural network, metallized film capacitor, parameter optimization

中图分类号:

TP114.2

禹建丽, 黄鸿琦, 苗满香. 基于主成分分析与神经网络的多响应参数优化[J]. 系统仿真学报, 2018, 30(1): 176-184.

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.

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