| 魏弦.基于核主成分分析的热误差模型自变量优化[J].电子测量与仪器学报,2017,31(12):2017-2022 |
| 基于核主成分分析的热误差模型自变量优化 |
| Independence variable optimization of thermal error model based on KPCA |
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| DOI：10.13382/j.jemi.2017.12.019 |
| 中文关键词: 数控机床 热误差 自变量优化 核主成分 误差补偿 |
| 英文关键词:CNC machine tool thermal error independence variable optimization KPCA error compensation |
| 基金项目:国家自然科学基金（51375382）、四川省科技厅科技支撑计划（2016GZ0205）、四川省教育厅重点项目（16ZA0415）、攀枝花学院博士基金（BKQJ2017007）资助项目 |
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| 摘要点击次数: 3185 |
| 全文下载次数: 7131 |
| 中文摘要: |
| 为了改善主成分分析对温度场的非线性数据提取能力不足的问题，提出一种核主成分温测点优化法。引入非线性映射函数，将输入的温度数据向特征空间映射，选择高斯径向基为核函数，将特征空间的内积运算变换为输入空间的核函数运算，求出特征值和核特征向量，建立综合自变量。在一台数控加工中心上进行试验，将提出方法建立的预测模型和主成分分析获得的预测模型进行比较，均方根误差降低约36%，最大残差降低29%，结果表明，采用核主成分法建模，能更好提取温度数据特征，可以使试验机床进给系统的热误差预测能力显著提升。 |
| 英文摘要: |
| To address the issue that principle component analysis (PCA)shows a poor ability to acquire the characteristic of nonlinearity data, a kernel principle component analysis(KPCA) temperature point optimization method is proposed. Firstly, nonlinearity mapping function is introduced to map the input temperature data into the characteristic space,and a Gaussian radial basis is selected to be a kernel function. Secondly, inner product operation in characteristic space is transformedinto kernel function operation in input space, eigenvalues and kernel eigenvectors are found. Finally, a comprehensive independent variable is formed. According to an experiment conducted on a CNC machine center,and comparedwith the PCA model, RMSE and Maximum residual error reduces by 36% and 29%, respectively.KPCA can preferably acquire the characteristic of temperature data, and the prediction ability of KPCA model has an obvious improvement. |
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