Dynamic Error Correction of Measuring System Using Support Vector Machine

摘要：

The support vector machine (SVM) is proposed for dynamic error correction of measuring systems. The SVM is established based on the structural risk minimization principle rather than minimize the empirical error commonly implemented in the neural networks. Hence, the SVM can overcome the shortcoming of neural networks in dynamic error correction of measuring systems. The feasibility and efficacy of the method are demonstrated by applying it to an example. The results show that the proposed method is still effective even if there is additive measuring noise.

关键词： error correction measuring system support vector machine

作者: WANG Xiaodong ZHANG Haoran ZHANG Changjiang WANG Jinshan YE Meiying

作者单位: Department of Electronic Engineering, Zhejiang Normal University, Jinhua 321004, China

会议类型: 国际会议

会议名称: 第七届国际测试技术研讨会

会议地点: 北京

会议语种:英文

在线出版日期: 2007-08-05（万方平台首次上网日期，不代表论文的发表时间）