基于最小二乘支持向量机的刀具磨损预报建模

Tool wear prediction modeling based on least squares support vector machine

最小二乘支持向量机是支持向量机的一种扩展,已在模式识别和函数逼近等领域得到了成功应用。将最小二乘支持向量机应用于刀具磨损的预报,在网格搜索的基础上采用交叉验证法确定模型的参数,并同偏最小二乘回归的建模结果进行了比较。

Least square support vector machine （LS-SVM） is an expansion of support vector machine, which has been successfully applied to pattern recognition and fuoction approximation. In this paper LS-SVM was applied to tool wear prediction modeling. Model parameters were determined by cross validation based on grid search and the result was compared with partial least square regress.