An AGO-SVM drift modelling method for a dynamically tuned gyroscope

摘要

In this paper, the support vector machine (SVM), a novel learning machine based on statistical learning theory (SLT), is described and applied in the drift modelling of the dynamically tuned gyroscope (DTG). As a data preprocessing method, accumulated generating operation (AGO) is applied to the SVM for further improving the modelling precision and the learning performance of the drift model. The grey modelling method and RBF neural network are also investigated as a comparison to the SVM and AGO-SVM modelling methods. The modelling results of the real drift data from the long-term measurement system of a DTG indicate that the SVM method is available practically in the modelling of DTG drift and the proposed strategy of combining SVM with AGO is effective in improving the modelling precision and the learning performance.