Open AccessExploring Neural Network to Predict Car Tyre Inflation Time and Power Requirement of a Tyre Pressure Control Unit.
Author(s) -
Semiu Taiwo Amosun,
Olusegun David Samuel,
P. T. Zubairu,
Bukola Olalekan Bolaji,
Sunday Ojo Fayomi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1378/3/032080
Subject(s) - artificial neural network , inflation (cosmology) , power (physics) , genetic algorithm , computer science , automotive engineering , control (management) , engineering , machine learning , artificial intelligence , physics , quantum mechanics , theoretical physics
This study used Artificial Neural Network (ANN) for the prediction of power required to inflate different tyre sizes and inflation times. ANN is a widely accepted machine learning method that uses past data to predict future trend. An existing database obtained experimentally from a tyre pressure control test rig was optimized using genetic algorithm(GA) which is an optimization tool that can find better subsets of input variables for importing into ANN. The ANN results were compared with the results obtained experimentally. The results show that the model can be implemented in modern day tyre pressure control designs and be used to predict inflation times and power required to inflate different tyre sizes.