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

10 pages•Published: March 13, 2019

Ana Farhat, Kyle Hagen, Ka C Cheok and Balaji Boominathan

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 Levenberg- Marquardt Algorithm (LMA) as the optimization algorithms. Finally, the performance of identified model has been evaluated.

Keyphrases: canfis, electronic brake system (ebs), neuro fuzzy based identification

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 444-453.

Links:	https://easychair.org/publications/paper/6P1D
	https://doi.org/10.29007/q7pr

BibTeX entry

@inproceedings{CATA2019:Neuro_fuzzy_based_Electronic,
  author    = {Ana Farhat and Kyle Hagen and Ka C Cheok and Balaji Boominathan},
  title     = {Neuro-fuzzy-based Electronic Brake System Modeling using Real Time Vehicle Data},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/6P1D},
  doi       = {10.29007/q7pr},
  pages     = {444-453},
  year      = {2019}}

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