Parametric and non-parametric identification for an automotive air conditioning system
This research aims to develop the dynamic model of an Automotive Air Conditioning system using conventional and intelligent techniques. The research focused to achieve the optimal model that can effectively capture the behavior of the system. Linear and Non-Linear Autoregressive with Exogenous input...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.utm.my/91169/1/IntanZMatDarus2019_ParametricandNonParametricIdentification.pdf |
| _version_ | 1796865174560309248 |
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| author | Al-Awad, M. A. Ghani, Z. A. Mat Darus, I. Z. |
| author_facet | Al-Awad, M. A. Ghani, Z. A. Mat Darus, I. Z. |
| author_sort | Al-Awad, M. A. |
| collection | ePrints |
| description | This research aims to develop the dynamic model of an Automotive Air Conditioning system using conventional and intelligent techniques. The research focused to achieve the optimal model that can effectively capture the behavior of the system. Linear and Non-Linear Autoregressive with Exogenous input (ARX and NARX) and Linear Autoregressive Moving Average with Exogenous inputs (ARMAX) models were used to capture the dynamics behavior of the system using system identification technique utilizing experimentally acquired input-output data. The system identifications were conducted using parametric and conventional method namely Recursive Least Squares (RLS) and Recursive Extended Least Squares (RELS), and nonparametric method using Intelligent algorithm of Multilayer Perceptron Neural Network. The comparative investigations have proven the superiority of the ARMAX model over the ARX and NARX model in term of prediction performance, whiting the disturbance as well as computational load for training. The mean square error are 2.7341×10-4, 1.9017×10-5 and 5.0257×10-6, for ARX, NARX, and ARMAX model respectively. |
| first_indexed | 2024-03-05T20:52:56Z |
| format | Conference or Workshop Item |
| id | utm.eprints-91169 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:52:56Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | utm.eprints-911692021-06-21T08:40:37Z http://eprints.utm.my/91169/ Parametric and non-parametric identification for an automotive air conditioning system Al-Awad, M. A. Ghani, Z. A. Mat Darus, I. Z. TJ Mechanical engineering and machinery This research aims to develop the dynamic model of an Automotive Air Conditioning system using conventional and intelligent techniques. The research focused to achieve the optimal model that can effectively capture the behavior of the system. Linear and Non-Linear Autoregressive with Exogenous input (ARX and NARX) and Linear Autoregressive Moving Average with Exogenous inputs (ARMAX) models were used to capture the dynamics behavior of the system using system identification technique utilizing experimentally acquired input-output data. The system identifications were conducted using parametric and conventional method namely Recursive Least Squares (RLS) and Recursive Extended Least Squares (RELS), and nonparametric method using Intelligent algorithm of Multilayer Perceptron Neural Network. The comparative investigations have proven the superiority of the ARMAX model over the ARX and NARX model in term of prediction performance, whiting the disturbance as well as computational load for training. The mean square error are 2.7341×10-4, 1.9017×10-5 and 5.0257×10-6, for ARX, NARX, and ARMAX model respectively. 2019 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/91169/1/IntanZMatDarus2019_ParametricandNonParametricIdentification.pdf Al-Awad, M. A. and Ghani, Z. A. and Mat Darus, I. Z. (2019) Parametric and non-parametric identification for an automotive air conditioning system. In: 2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019, 17-19 Oct 2019, Dublin, Ireland. http://www.dx.doi.org/10.1145/3358331.3358384 |
| spellingShingle | TJ Mechanical engineering and machinery Al-Awad, M. A. Ghani, Z. A. Mat Darus, I. Z. Parametric and non-parametric identification for an automotive air conditioning system |
| title | Parametric and non-parametric identification for an automotive air conditioning system |
| title_full | Parametric and non-parametric identification for an automotive air conditioning system |
| title_fullStr | Parametric and non-parametric identification for an automotive air conditioning system |
| title_full_unstemmed | Parametric and non-parametric identification for an automotive air conditioning system |
| title_short | Parametric and non-parametric identification for an automotive air conditioning system |
| title_sort | parametric and non parametric identification for an automotive air conditioning system |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utm.my/91169/1/IntanZMatDarus2019_ParametricandNonParametricIdentification.pdf |
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