Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 Articles in Press 2026 05 13 Predicting Torque Ripple and Average Torque of a Switched Reluctance Motor Using MLP and RBF Models 6033 10.22060/eej.2026.25442.5934 EN Siroos Hemmati Kermanshah University of Technology, Kermanshah, Iran 0000-0002-6056-716X Milad Goldasteh Technical National University, Kermanshah, Iran Mohsen Asadi Razi university, Kermanshah, Iran Seyed Shahrooz Hosseini Azad University, Kermanshah, Iran Sobhan Roshani Azad University, Kermanshah, Iran Journal Article 2026 01 27 <span>The optimal design of a Switched Reluctance Motor (SRM) requires an accurate model.<span> </span>Analytical models presented for SRM do not meet required accuracy. Approximate models also have varying degrees of accuracy in predicting SRM characteristics. Hence, in this paper, two Neural Network (NN) models, Radial Basis Function (RBF) and Multilayer Perceptron (MLP), have been proposed to predict torque ripple and average torque, respectively. To train and test the models, 100 samples were extracted, 90% for training and 10% for testing. Furthermore, the Finite Element Method (FEM) has been used to solve the samples. The influencing parameters of the proposed NN models (number of hidden layers, number of neurons in hidden layers, bias, etc.) also have been determined to achieve the desired accuracy and minimal complexity. To evaluate the performance of the models, two criteria, Root Mean Square Error (RMSE) and Mean Relative Error (MRE), have been used. Both criteria indicate that the MLP model is successful in predicting torque ripple, while the RBF model excels in predicting average torque.</span> https://eej.aut.ac.ir/article_6033_317d17f10845da500bcf49780b7f35bf.pdf