CAJ | 학술논문

最小二乘支持向量机(LS-SVM)模型是表征数控机床热误差特性的有效工具，但该模型中的参数设置直接影响建模的精度。传统的基于交叉验证法或网格法的参数获取方法存在计算量大、精度低的缺点，且同一组模型常数往往不能准确表征机床多种工况条件下所产生的热误差。为解决这一问题，提出了一种基于贝叶斯证据框架理论的LS-SVM多工况参数优化方法。通过测量不同工况下数控机床温度值与主轴热变形量，采用贝叶斯证据框架的3个推断对LS-SVM模型进行训练并对参数进行辨识和优化，推导出了不同工况所对应的最优模型和参数。热误差建模实验验证了该参数优化方法的有效性，结果显示，经优化的模型具有泛化能力强、预测精度高、计算速度快的特点，能够较准确地描述多种典型工况条件下的实际热误差特性。
최소이승지지향량궤(LS-SVM)모형시표정수공궤상열오차특성적유효공구，단해모형중적삼수설치직접영향건모적정도。전통적기우교차험증법혹망격법적삼수획취방법존재계산량대、정도저적결점，차동일조모형상수왕왕불능준학표정궤상다충공황조건하소산생적열오차。위해결저일문제，제출료일충기우패협사증거광가이론적LS-SVM다공황삼수우화방법。통과측량불동공황하수공궤상온도치여주축열변형량，채용패협사증거광가적3개추단대LS-SVM모형진행훈련병대삼수진행변식화우화，추도출료불동공황소대응적최우모형화삼수。열오차건모실험험증료해삼수우화방법적유효성，결과현시，경우화적모형구유범화능력강、예측정도고、계산속도쾌적특점，능구교준학지묘술다충전형공황조건하적실제열오차특성。
LS-SVM is an effective tool for machine error modeling.The traditional methods to set the parameters of LS-SVM which determined the modeling accuracy included ten-fold cross validation and grid method.However,using these methods,parameter calculation was complex and prone to low accuracy.Moreover,the LS-SVM model with only one set of parameters was hard to precisely describe the thermal error behaviors under the different work conditions.In order to solve these prob-lems,based on Bayesian evidence framework a novel method was proposed to identify and optimize LS-SVM parameters under multiple working conditions.Three inferring levels of Bayesian evidence framework were used to derive the optimal model parameters corresponding to the different operating conditions.A series of experiments for thermal error modeling verified the validity of this method. LS-SVM model based on Bayesian evidence framework has good generalization ability,accurate pre-diction,and rapid calculation speed,so it can describe the actual thermal error characteristics more ac-curately under multiple work conditions.