Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression

This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (B _s ) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction.