Improving linearity of position-sensitive detector using support vector machines  

作　　者：叶美盈 

机构地区：[1]College of Mathematics and PhysicsZhejiang Normal University,Jinhua 321004

出　　处：《Chinese Optics Letters》2005年第4期205-207,共3页中国光学快报（英文版）

基　　金：This work was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. 602145.

摘　　要：An intelligent method for improving position linearity of position-sensitive detector (PSD), based on support vector machines (SVMs), is developed. The SVM is established based on the structural risk minimization principle rather than minimizing the empirical error commonly implemented in neural networks. SVM can achieve higher generalization performance. Training SVM is equivalent to solving a linearly constrained quadratic programming problem, thus the solution of SVM is always unique and globally optimal. The improving position linearity procedure has been illustrated using a two-dimensional (2D) PSD. It is pointed out that the position linearity of the measuring system with a proper SVM correction is improved by two orders of magnitude in the measurement range.An intelligent method for improving position linearity of position-sensitive detector (PSD), based on support vector machines (SVMs), is developed. The SVM is established based on the structural risk minimization principle rather than minimizing the empirical error commonly implemented in neural networks. SVM can achieve higher generalization performance. Training SVM is equivalent to solving a linearly constrained quadratic programming problem, thus the solution of SVM is always unique and globally optimal. The improving position linearity procedure has been illustrated using a two-dimensional (2D) PSD. It is pointed out that the position linearity of the measuring system with a proper SVM correction is improved by two orders of magnitude in the measurement range.

关 键 词：Neural networks Position measurement 

分 类 号：TN965[电子电信—信号与信息处理]