A new method for force prediction in an accelerometer force balance system using support vector regression

The accurate prediction of force is very important in the present scenario of aerodynamic force measurement. The high accuracy of force prediction during calibration facilitates a better accuracy of force measurement in aerodynamic facilities like shock tunnels and wind tunnels. The present study describes the force prediction in an accelerometer force balance system using support vector regression (SVR). The comparison of SVR with the existing force prediction techniques namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) has also been carried out. The accelerometer force balance used in the current experimentation consists of a tri-axial accelerometer to measure the response on an aluminium hemispherical model on the application of force. The impulse forces were applied along the axial, normal and azimuthal directions. The forces were predicted using the accelerations obtained from the tri-axial accelerometer. SVR method was able to predict the forces quite accurately as compared to ANFIS and ANN. However, SVR has the advantage over ANFIS and ANN in that it is independent of the magnitude of the training and testing data. It is capable of an accurate prediction of forces with any magnitude of training and testing data, unlike ANFIS and ANN.