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Injected fuel flow forecasting with Online Sequential Extreme Learning Machine

The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malay...

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Main Authors: Zuraidi, Saad, Muhammad Khusairi, Osman, Mohd Yusoff, Mashor, Prof. Dr.
Other Authors: zuraidi570@ppinang.uitm.edu.my
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2013
Subjects:
Fuel flow forecasting
Single hidden layer feedforward networks (SLFN)
Online Sequential - Extreme learning machine (OS-ELM)
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/29847
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spelling my.unimap-298472013-11-17T04:12:34Z Injected fuel flow forecasting with Online Sequential Extreme Learning Machine Zuraidi, Saad Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr. zuraidi570@ppinang.uitm.edu.my khusairi@ppinang.uitm.edu.my yusoff@unimap.edu.my Fuel flow forecasting Single hidden layer feedforward networks (SLFN) Online Sequential - Extreme learning machine (OS-ELM) The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia. This study deals with Online Sequential Extreme Learning Machine (OS-ELM) modeling of a gasoline engine to predict the injected fuel flow of the engine. The single hidden layer feedforward networks (SLFN) trained by OS-ELM algorithm was selected as a black box model for forecasting purposes. The algorithm is used to train a SLFN using a set of data consists of the running gasoline engine features such as speed, revolution, fuel volume, current fuel consumption, gear, distance to empty in volume, distance to empty in kilometer, current distance, and battery voltage. A total of 700 data were used in forecasting process. The effectiveness of the method has been demonstrated through analysis of the performance error of the fitted network using a mean square error (MSE) expressed in decibel (dB), the best learning mode, optimum number of hidden nodes and forecasting time. Promising result of maximum speed of forecasting has been achieve with – 62.00 dB of mean square error and 1-by-1 learning mode for the OS-ELM using sinusoidal activation function. 2013-11-17T04:12:34Z 2013-11-17T04:12:34Z 2012-06-18 Working Paper p. 267 - 274 978-967-5760-11-2 http://hdl.handle.net/123456789/29847 en Proceedings of the The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012); Universiti Malaysia Perlis (UniMAP)
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 Fuel flow forecasting
Single hidden layer feedforward networks (SLFN)
Online Sequential - Extreme learning machine (OS-ELM)
spellingShingle Fuel flow forecasting
Single hidden layer feedforward networks (SLFN)
Online Sequential - Extreme learning machine (OS-ELM)
Zuraidi, Saad
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Injected fuel flow forecasting with Online Sequential Extreme Learning Machine
description The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia.
author2 zuraidi570@ppinang.uitm.edu.my
author_facet zuraidi570@ppinang.uitm.edu.my
Zuraidi, Saad
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
format Working Paper
author Zuraidi, Saad
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
author_sort Zuraidi, Saad
title Injected fuel flow forecasting with Online Sequential Extreme Learning Machine
title_short Injected fuel flow forecasting with Online Sequential Extreme Learning Machine
title_full Injected fuel flow forecasting with Online Sequential Extreme Learning Machine
title_fullStr Injected fuel flow forecasting with Online Sequential Extreme Learning Machine
title_full_unstemmed Injected fuel flow forecasting with Online Sequential Extreme Learning Machine
title_sort injected fuel flow forecasting with online sequential extreme learning machine
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/29847
_version_ 1643795580515778560
score 13.222552

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