Neuro-fuzzy-based Electronic Brake System Modeling using Real Time Vehicle Data

Abstract

Electronic Brake System (EBS) is considered as one of the most complicated systems whose performance depends on the subsystems parameters. Usually these parameters are difficult to predict. Based on the task to improve the EBS performance, this article presents a mathematical modeling approach based on neuro-fuzzy network method to model a subsystem of EBS. For the model parameters identification, a neuro-fuzzy network has been implemented based on Least Square Error (LSE) and LevenbergMarquardt Algorithm (LMA) as the optimization algorithms. Finally, the performance of identified model has been evaluated.