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Modeling and forecasting of injected fuel flow using neural network

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Main Authors: Z., Saad, M. K., Osman, S., Omar, Mohd Yusoff, Mashor, Prof. Dr.
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2011
Subjects:
Injected fuel flow
Levenberg-Marquardt
Multilayered perceptron network
Neural networks
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/13968
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spelling my.unimap-139682011-09-30T12:34:27Z Modeling and forecasting of injected fuel flow using neural network Z., Saad M. K., Osman S., Omar Mohd Yusoff, Mashor, Prof. Dr. Injected fuel flow Levenberg-Marquardt Multilayered perceptron network Neural networks Link to publisher's homepage at http://ieeexplore.ieee.org/ The aim of this research is to develop an intelligent automated online forecasting of a car fuel consumption using neural network and classified it into classes of driving style. A new online monitoring tool was developed to acquire and analyze data collected from a car for the purpose of fuel consumption modelling and forecasting. The data was transmitted via ECU Can Bus attach to the car to the automotive single board computer. The online monitoring and forecasting tools were developed by using 8-bit Single-Chip Microcontroller as a data acquisition processor. Distance, speed, revolution, fuel flow, fuel consumption and temperature transducer are taped from the experimented car to gain the information. The multilayered perceptron network trained by Levenberg-Marquardt algorithm was selected as a black box model for forecasting purposes. The input variables were taped from car sensors. The data set consists of 2000 data samples. The first 1000 data were used for training and the rest were used in validation and forecasting process. Based on the best network execution, it was found that the best MSE during validation phase is about 0.0804 produced at the 26 hidden neurons. The results of the forecasting during training obviously show that during the first 200 data series the forecasting error is quite high but after 200 data series the neural network model have a tendency to improve quickly and forecast slightly the real value of the injected fuel flow. 2011-09-30T12:34:27Z 2011-09-30T12:34:27Z 2011-03-04 Working Paper p. 243-247 978-1-6128-4414-5 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5759880 http://hdl.handle.net/123456789/13968 en Proceedings of the IEEE 7th International Colloquium on Signal Processing and Its Applications (CSPA 2011) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Injected fuel flow
Levenberg-Marquardt
Multilayered perceptron network
Neural networks
spellingShingle Injected fuel flow
Levenberg-Marquardt
Multilayered perceptron network
Neural networks
Z., Saad
M. K., Osman
S., Omar
Mohd Yusoff, Mashor, Prof. Dr.
Modeling and forecasting of injected fuel flow using neural network
description Link to publisher's homepage at http://ieeexplore.ieee.org/
format Working Paper
author Z., Saad
M. K., Osman
S., Omar
Mohd Yusoff, Mashor, Prof. Dr.
author_facet Z., Saad
M. K., Osman
S., Omar
Mohd Yusoff, Mashor, Prof. Dr.
author_sort Z., Saad
title Modeling and forecasting of injected fuel flow using neural network
title_short Modeling and forecasting of injected fuel flow using neural network
title_full Modeling and forecasting of injected fuel flow using neural network
title_fullStr Modeling and forecasting of injected fuel flow using neural network
title_full_unstemmed Modeling and forecasting of injected fuel flow using neural network
title_sort modeling and forecasting of injected fuel flow using neural network
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/13968
_version_ 1643790855002128384
score 13.222552

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