Neuro-fuzzy identification of an internal combustion engine

Dynamic modeling and identification of an internal combustion engine (ICE) model is presented in this paper. Initially, an analytical model of an internal combustion engine simulated within SIMULINK environment is excited by pseudorandom binary sequence (PRBS) input. This random signals input is ch...

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Main Authors:	Tuan Kamaruddin, Tengku Nordayana Akma, Mat Darus, Intan Z
Format:	Article
Language:	English
Published:	United Kingdom Simulation Society 2012
Subjects:	TJ212 Control engineering TL1 Motor vehicles
Online Access:	http://irep.iium.edu.my/78965/ http://irep.iium.edu.my/78965/ http://irep.iium.edu.my/78965/1/NeuroFuzzy.pdf

id	iium-78965
recordtype	eprints
spelling	iium-789652020-03-16T07:27:18Z http://irep.iium.edu.my/78965/ Neuro-fuzzy identification of an internal combustion engine Tuan Kamaruddin, Tengku Nordayana Akma Mat Darus, Intan Z TJ212 Control engineering TL1 Motor vehicles Dynamic modeling and identification of an internal combustion engine (ICE) model is presented in this paper. Initially, an analytical model of an internal combustion engine simulated within SIMULINK environment is excited by pseudorandom binary sequence (PRBS) input. This random signals input is chosen to excite the dynamic behavior of the system over a large range of frequencies. The input and output data obtained from the simulation of the analytical model is used for the identification of the system. Next, a parametric modeling of the internal combustion engine using recursive least squares (RLS) technique within an auto-regressive external input (ARX) model structure and a nonparametric modeling using neuro-fuzzy modeling (ANFIS) approach are introduced. Both parametric and nonparametric models verified using one-step-ahead (OSA) prediction, mean squares error (MSE) between actual and predicted output and correlation tests. Although both methods are capable to represent the dynamic of the system very well, it is demonstrated that ANFIS gives better prediction results than RLS in terms of mean squares error achieved between the actual and predicted signals. United Kingdom Simulation Society 2012-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/78965/1/NeuroFuzzy.pdf Tuan Kamaruddin, Tengku Nordayana Akma and Mat Darus, Intan Z (2012) Neuro-fuzzy identification of an internal combustion engine. International Journal of Simulation Systems, 13 (3B). pp. 30-37. ISSN 1473-804X E-ISSN 1473-8031 https://ijssst.info/Vol-13/No-3B/paper5.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TJ212 Control engineering TL1 Motor vehicles
spellingShingle	TJ212 Control engineering TL1 Motor vehicles Tuan Kamaruddin, Tengku Nordayana Akma Mat Darus, Intan Z Neuro-fuzzy identification of an internal combustion engine
description	Dynamic modeling and identification of an internal combustion engine (ICE) model is presented in this paper. Initially, an analytical model of an internal combustion engine simulated within SIMULINK environment is excited by pseudorandom binary sequence (PRBS) input. This random signals input is chosen to excite the dynamic behavior of the system over a large range of frequencies. The input and output data obtained from the simulation of the analytical model is used for the identification of the system. Next, a parametric modeling of the internal combustion engine using recursive least squares (RLS) technique within an auto-regressive external input (ARX) model structure and a nonparametric modeling using neuro-fuzzy modeling (ANFIS) approach are introduced. Both parametric and nonparametric models verified using one-step-ahead (OSA) prediction, mean squares error (MSE) between actual and predicted output and correlation tests. Although both methods are capable to represent the dynamic of the system very well, it is demonstrated that ANFIS gives better prediction results than RLS in terms of mean squares error achieved between the actual and predicted signals.
format	Article
author	Tuan Kamaruddin, Tengku Nordayana Akma Mat Darus, Intan Z
author_facet	Tuan Kamaruddin, Tengku Nordayana Akma Mat Darus, Intan Z
author_sort	Tuan Kamaruddin, Tengku Nordayana Akma
title	Neuro-fuzzy identification of an internal combustion engine
title_short	Neuro-fuzzy identification of an internal combustion engine
title_full	Neuro-fuzzy identification of an internal combustion engine
title_fullStr	Neuro-fuzzy identification of an internal combustion engine
title_full_unstemmed	Neuro-fuzzy identification of an internal combustion engine
title_sort	neuro-fuzzy identification of an internal combustion engine
publisher	United Kingdom Simulation Society
publishDate	2012
url	http://irep.iium.edu.my/78965/ http://irep.iium.edu.my/78965/ http://irep.iium.edu.my/78965/1/NeuroFuzzy.pdf
first_indexed	2023-09-18T21:51:08Z
last_indexed	2023-09-18T21:51:08Z
_version_	1777413776288514048

Neuro-fuzzy identification of an internal combustion engine

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