基于径向基神经网络的光栅细分方法

为了研究细分精度高、位移跟踪速度快的光栅位移测量系统，提出一种基于径向基神经网络的光栅细分方法.利用三层RBF神经网络，在一个莫尔条纹信号周期内取多个样本点，并把多个样本点所对应的正切值作为网络的输入，将该样本点在一个栅距内的微位移量作为目标输出，建立合理的神经网络模型，与DSP相结合实现莫尔条纹细分.通过对样本点的分段学习，证明了仅用少量的神经元即可实现高精度细分.该神经网络结构简单，非线性逼近能力强，通过对非样本点数据的实验验证，证明了该系统的可行性，具有一定的实用价值.

Grating subdivision method based on radial basis function neural network

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.