基于PLS和改进CVR的数控机床热误差建模  

Thermal Error Modeling of CNC Machine Tool Based on Partial Least Squares and Improved Core Vector Regression

作　　者：余文利[1] 姚鑫骅[2] 孙磊[2] 傅建中[2] 

机构地区：[1]衢州职业技术学院信息工程学院,衢州324000 [2]浙江大学机械工程学系,杭州310027

出　　处：《农业机械学报》2015年第2期357-364,共8页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(551105336);浙江省自然科学基金资助项目(Y1100281);浙江省重点科技创新团队计划资助项目(2009R50008);浙江省科技厅公益性应用研究计划资助项目(2014C31089)

摘　　要：为提高支持向量回归(SVR)模型的预测能力,将核心向量回归(Core vector regression,CVR)方法引入到数控机床热误差建模中,并采用偏最小二乘(Partial least squares,PLS)算法从输入样本提取主成分,构建特征集,然后使用改进的粒子群优化(Improved particle swam optimization,IPSO)算法对CVR的模型参数进行寻优,从而提出一种基于PLS-IPSO-CVR的数控机床热误差建模方法。仿真实验表明,所提出的建模方法在预测精度和速度方面优于传统SVR模型和BP神经网络模型,从而验证了组合建模方法的可行性和有效性。Support vector regression （SVR） is an effective tool for machine error modeling. To improve the predicted performance of SVR model, the core vector regression （CVR） algorithm which is suitable for resolving the training of large-scale sample data was introduced into thermal error modeling for CNC machine tool. Principal components were firstly extracted from the sample set using the feature extraction of partial least squares （PLS） algorithm to construct the feature set, which would reduce the number of state variables without information loss by dimension reduction, data de-noising and eliminating the correlative between variables. Then improved particle swam optimization （IPSO） was applied for determining the parameters of CVR to get the optimal performance of the thermal error model, and the proposed combined method was called PLS - IPSO - CVR. Experimental results showed that the training speed of PLS - IPSO - CVR model was much faster and it produced fewer support vectors on very large sample data in comparison with SVR and BP neural network. Thus the feasibility and effectiveness of this combined modeling method was verified.

关 键 词：数控机床 热误差建模 偏最小二乘 特征提取 核心向量回归 改进粒子群优化 

分 类 号：TH161.4[机械工程—机械制造及自动化]