Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR

A novel machine learning method based on support vector regression (SVR) approach, combined with a particle swarm optimization (PSO) algorithm for its parameter optimization, was proposed to predict the magnetic field in the centre of a superconducting solenoid surrounded by a cold iron yoke in terms of the geometrical parameters of the yoke. The leave-one-out cross validation (LOOCV) test results of SVR reveal that the prediction ability of the SVR model is greater than that of the conventional multivariate nonlinear regression. The maximum absolute percentage error of 26 samples obtained by SVR did not exceed 0.50% and the statistical mean absolute percentage error was solely 0.05%, which was quite accurate and satisfactory with the requirement of ultraprecision engineering and manufacturing. This investigation provides a clue that the hybrid PSO-SVR approach elaborated in this paper is a promising and practical methodology to precisely design the physical dimension of the iron yoke surrounded around the superconducting solenoid.