摘要
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.
出处
《武汉工业学院学报》
CAS
2009年第2期112-114,118,共4页
Journal of Wuhan Polytechnic University
关键词
最小二乘
支持向量机
刀具磨损
预报
least square
support vector machine
tool wear
prediction