Grating-scale-measuring-error dynamic compensation method based on deep learning

Abstract

The invention discloses a grating-scale-measuring-error dynamic compensation method based on deep learning. The rating-scale-measuring-error dynamic compensation method includes the steps that error data is collected and obtained, and meanwhile effect intensity values of a plurality of interference factors corresponding to the error data are measured and obtained through a plurality of sensors; the error data is decomposed into a plurality of IMF components based on the empirical mode decomposition algorithm, and Hilbert marginal spectrums of all the IMF components are solved and obtained; the effect intensity values of the multiple interference factors corresponding to the error data and the Hilbert marginal spectrums of the multiple IMF components serve as input data, and identification and calculation are carried out through a trained CNN neural network to obtain correspondingly-output label functions; trend terms corresponding to the error data are obtained and accumulated to serve as the error compensation value of a grating scale; the grating scale is subjected to measurement compensation through the obtained error compensation value. The grating-scale-measuring-error dynamic compensation method is easy to operate, low in cost and good in compensation effect, effective compensation of a grating-scale system can be achieved, and the grating-scale-measuring-error dynamic compensation method can be widely applied to grating-scale measuring industries.

Description

technical field [0001] The invention relates to the field of grating ruler error measurement, in particular to a dynamic compensation method for grating ruler measurement errors based on deep learning. Background technique [0002] CNN neural network: convolutional neural network; [0003] Empirical Mode Decomposition: Empirical Mode Decomposition, referred to as EMD, is an algorithm for signal analysis and processing. The component contains local characteristic signals of different time scales of the original signal; [0004] IMF: Intrinsic Mode Function, Intrinsic Mode Function, a signal that meets certain conditions generated after the signal is decomposed by EMD. Usually, after EMD decomposition decomposes the signal, n IMF components and a residual component will be generated, which can also be called Obtain n+1 IMF components; [0005] HMS: Hilbert marginal spectrum, Hilbert marginal spectrum, a spectrogram; [0006] mini-batch: refers to the batch data set of the ...

AI Technical Summary

Problems solved by technology

The disadvantage of this type of method is that a basis function or function form is specified in advance, and then the function parameters are determined through calculation, which will destroy the structural characteristics of the signal itself and cannot be accurately compensated.

Therefore, the current processing method cannot compensate the trend error, and it is difficult to effectively improve the measurement accuracy of the grating ruler, which limits the application of the grating ruler

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