基于Online LS-SVM的钢铁件渗碳层深度在线检测
Modern Manufacturing Engineering（2009）
Abstract
为实现钢铁件渗碳层深度的在线电磁无损检测,提出在线最小二乘支持向量机(Online Least Square Support Vector Machine,Online LS-SVM)的建模方法。Online LS-SVM是以增量学习训练SVM,以减量学习减少样本数,实现小样本估计的训练方法。实验结果表明,Online LS-SVM不仅能实现钢铁件渗碳层深度的在线电磁无损检测,而且具有学习速度快、泛化性能好和对样本依赖程度低的优点。
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Key words
online detecting,Least Square Support Vector Machine(LS-SVM),Artificial Neural Network(ANN),carburizing,Electromagnetic Nondestructive Testing(ENDT)
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