Choosing of neural network architecture for electronic differential system of electric vehicle

Aim. To study various network architecture options for implementing an electronic differential system in an electric vehicle. Materials and Methods. The study primarily used comparative analysis to identify the most rational neural network (NN) architecture for processing numerical data structured as arrays. Results. The analysis revealed that a deep learning neural network is the most effective choice. For future developments, and after experimental confirmation, a recurrent neural network could also be a viable option. Conclusion. The study confirmed that achieving the desired goal is not feasible using convolutional neural networks, large language models, random vector functional communication networks and radial-basis functional NNs.