Deep Learning-Based Magnetic Core Loss Prediction Using a Fully Connected Neural Network

Abstract

This paper proposes a core loss prediction model based on a fully connected neural network (FCNN). After pre-processing the data, seven key features are extracted, and feature importance is sorted. The excitation waveform features are converted into five variables and combined with four additional features to form the final input feature set. Based on this, the FCNN prediction model is constructed, the early stop method is used in the training process to prevent overfitting, and the generalization ability is evaluated using cross-validation[1]. The R2 of the model training set and the test set are 0.9889 and 0.9843, respectively, and the prediction error of 94.55% of the test samples is less than ±10%. FCNN shows higher accuracy and stability in core loss prediction tasks than traditional machine learning methods.