Title

Neural network modeling for fuel consumption base on least computational cost parameters

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019

Publication Date

11-1-2019

Abstract

Fuel consumption are important in every vehicle. This study investigates the performance of fuel usage in an engine. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling. Back propagation neural network was used to determine the optimized fuel consumption. There is a lot of factor which has an effect to fuel consumption in conventional drive procedure, however in this study the factors affecting the fuel consumption are the distance, time, acceleration, and velocity of a car. These parameters are used as input information for the neural network training and fuel consumption prediction as output. This study shows the ANN capability to predict the fuel consumption using MATLAB neural fitting tool. The result demonstrated that the system using neural network is efficient for predicting fuel consumption of an engine. © 2019 IEEE.

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Digitial Object Identifier (DOI)

10.1109/HNICEM48295.2019.9072728

Disciplines

Electrical and Electronics | Power and Energy

Keywords

Energy consumption; Neural networks (Computer science)

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