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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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United Kingdom Simulation Society
2012
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| Online Access: | http://irep.iium.edu.my/78965/ http://irep.iium.edu.my/78965/1/NeuroFuzzy.pdf |
| _version_ | 1848788696888770560 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:44:56Z |
| format | Article |
| id | iium-78965 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T17:44:56Z |
| publishDate | 2012 |
| publisher | United Kingdom Simulation Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 |
| spellingShingle | TJ212 Control engineering TL1 Motor vehicles Tuan Kamaruddin, Tengku Nordayana Akma Mat Darus, Intan Z Neuro-fuzzy identification of an internal combustion engine |
| title | 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_short | Neuro-fuzzy identification of an internal combustion engine |
| title_sort | neuro-fuzzy identification of an internal combustion engine |
| topic | TJ212 Control engineering TL1 Motor vehicles |
| url | http://irep.iium.edu.my/78965/ http://irep.iium.edu.my/78965/ http://irep.iium.edu.my/78965/1/NeuroFuzzy.pdf |