A hybrid grey-based model for drift signal of DTG

This paper discusses several grey-based models for reducing model errors of a dynamically tuned gyroscope (DTG). Wavelet transform (WT), linear regression techniques are respectively integrated into the grey model to enhance modelling capability of the GM (1,1), a single variable first-order grey model. The DTG original drift data are preprocessed by the WT such that the disturbing impactive noises of drift data can be removed. The post-processing data are then used to construct the grey model. Due to the inherent error between the modelling values and actual outputs, the grey model is further compensated by the linear regression method. Numerical results from long-term measured x-axis and y-axis drift data of a DTG verify the effectiveness of the proposed hybrid strategies. Neural network modelling method is also investigated to provide a comparison.