沈阳工业大学学报2011，Vol.33Issue(2)：193-197,5.
基于径向基神经网络的光栅细分方法
Grating subdivision method based on radial basis function neural network
摘要
Abstract
In order to develop the grating displacement measuring system with higher subdivision accuracy and displacement tracking speed, a grating subdivision method based on radial basis function neural network was proposed. The multiple sample points in one moiré signal period were taken out using three-layer RBF neural network. The tangent values corresponding to the multiple sample points were taken as the input of the network and the micro displacement of the sample point in a grating pitch was regarded as the target output. The rational neural network model was established and combined with DSP to achieve the moiré fringe subdivision. Through the fractional learning of sample point, it is demonstrated that the high precision subdivision can be realized only with a few neurons. The structure of this neural network is simple and the ability of nonlinear approximation is powerful. The experiments of non-sample points prove that the system is feasible, and has application value.关键词
光栅传感器/莫尔条纹/细分/乘法倍频/RBF神经网络/多项式拟合/DSP芯片/Matlab仿真分类
信息技术与安全科学引用本文复制引用
郭雨梅,关蕊,钟媛..基于径向基神经网络的光栅细分方法[J].沈阳工业大学学报,2011,33(2):193-197,5.基金项目
辽宁省科技攻关资助项目(2006219005) （2006219005）
沈阳市科技局科技支撑计划资助项目(1081229-1-00). （1081229-1-00）
