EMD- and SVM-based temperature drift modeling and compensation for a dynamically tuned gyroscope (DTG)
In this paper, support vector machine (SVM) is described and applied in the temperature drift modeling and compensation to reduce the influence of temperature variation on the output of dynamically tuned gyroscope (DTG) and to enhance its precision. To improve the modeling capability, empirical mode decomposition (EMD) is introduced into the SVM model to eliminate any impactive noises. The real temperature drift data set from the long-term measurement system of a certain DTG is employed to validate the effectiveness of the proposed combination model. The modeling and compensation results indicate that the proposed EMD-SVM model outperforms the neural network (NN) and single SVM models, and is feasible and effective in temperature drift modeling and compensation of the DTG.
emd svm temperature drift dynamically tuned gyroscope neural network