会议专题

Dynamic Error Correction of Measuring System Using Support Vector Machine

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.

error correction measuring system support vector machine

WANG Xiaodong ZHANG Haoran ZHANG Changjiang WANG Jinshan YE Meiying

Department of Electronic Engineering, Zhejiang Normal University, Jinhua 321004, China

国际会议

第七届国际测试技术研讨会

北京

英文

2007-08-05(万方平台首次上网日期,不代表论文的发表时间)