  The paper discusses the improvement of the accuracy of an inertial navigation
system created on the basis of MEMS sensors using machine learning (ML)
methods. As input data for the classifier, we used infor-mation obtained from a
developed laboratory setup with MEMS sensors on a sealed platform with the
ability to adjust its tilt angles. To assess the effectiveness of the models,
test curves were constructed with different values of the parameters of these
models for each core in the case of a linear, polynomial radial basis function.
The inverse regularization parameter was used as a parameter. The proposed
algorithm based on MO has demonstrated its ability to correctly classify in the
presence of noise typical for MEMS sensors, where good classification results
were obtained when choosing the optimal values of hyperpa-rameters.
