A BP Neural Network Approach for Accurate Calculation of Time-Varying Inductance Gradient in ERL

The inductance gradient is a vital parameter in the electromagnetic rail launcher, holding significant sway over the electromagnetic force and operational efficiency. Traditionally, the inductance gradient has been assumed to maintain a constant value for the sake of simplicity. However, in reality, it dynamically transforms throughout the launching process. Thus, achieving a precise conclusion for the time-varying inductance gradient becomes an utmost priority for the electromagnetic driving force during launch. Therefore, this article presents a novel approach utilizing the backpropagation (BP) neural network to address the challenge of accurately determining the time-varying inductance gradient. The testing set exhibits exceptional performance with an R-square (R2) value of 0.99982, indicating a high degree of correlation. The mean absolute error (MAE) is measured at 0.61273, highlighting the accuracy of the neural network in predicting values. Furthermore, the mean bias error (MBE) is found to be −0.033674, indicating a minimal systematic deviation. These results underscore the suitability of the BP neural network for precise calculations of inductance gradients. The effectiveness of the proposed approach is validated through a rigorous comparative analysis, comparing the obtained results with those reported in diverse scientific literature.