Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin
System Identification (SI) is a control engineering discipline concerned with the discovery of mathematical models based on dynamic measurements collected from the system. It is an important discipline in the construction and design of controllers, as SI can be used for understanding the properties...
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| Format: | Book Section |
| Language: | English |
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Institute of Graduate Studies, UiTM
2014
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| Online Access: | https://ir.uitm.edu.my/id/eprint/19441/ |
| _version_ | 1848804770281684992 |
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| author | Mohd Yassin, Ahmad Ihsan |
| author_facet | Mohd Yassin, Ahmad Ihsan |
| author_sort | Mohd Yassin, Ahmad Ihsan |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | System Identification (SI) is a control engineering discipline concerned with the discovery of mathematical models based on dynamic measurements collected from the system. It is an important discipline in the construction and design of controllers, as SI can be used for understanding the properties of the system as well as to forecast its behavior under certain past inputs and/or outputs. The NARMAX model and its derivatives (Nonlinear Auto-Regressive with Exogenous Inputs (NARX) and Nonlinear Auto-Regressive Moving Average (NARMA)) are powerful, efficient and unified representations of a variety of nonlinear models. The identification process of NARX/NARMA/NARMAX involves structure selection and parameter estimation, which can be simultaneously performed using the widely accepted Orthogonal Least Squares (OLS) algorithm. |
| first_indexed | 2025-11-14T22:00:25Z |
| format | Book Section |
| id | uitm-19441 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T22:00:25Z |
| publishDate | 2014 |
| publisher | Institute of Graduate Studies, UiTM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-194412018-06-11T07:49:58Z https://ir.uitm.edu.my/id/eprint/19441/ Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin Mohd Yassin, Ahmad Ihsan Malaysia System Identification (SI) is a control engineering discipline concerned with the discovery of mathematical models based on dynamic measurements collected from the system. It is an important discipline in the construction and design of controllers, as SI can be used for understanding the properties of the system as well as to forecast its behavior under certain past inputs and/or outputs. The NARMAX model and its derivatives (Nonlinear Auto-Regressive with Exogenous Inputs (NARX) and Nonlinear Auto-Regressive Moving Average (NARMA)) are powerful, efficient and unified representations of a variety of nonlinear models. The identification process of NARX/NARMA/NARMAX involves structure selection and parameter estimation, which can be simultaneously performed using the widely accepted Orthogonal Least Squares (OLS) algorithm. Institute of Graduate Studies, UiTM 2014 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/19441/1/ABS_AHMAD%20IHSAN%20MOHD%20YASSIN%20TDRA%20VOL%206%20IGS_14.pdf Mohd Yassin, Ahmad Ihsan (2014) Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin. (2014) In: The Doctoral Research Abstracts. IPSis Biannual Publication, 6 (6). Institute of Graduate Studies, UiTM, Shah Alam. |
| spellingShingle | Malaysia Mohd Yassin, Ahmad Ihsan Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin |
| title | Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin |
| title_full | Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin |
| title_fullStr | Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin |
| title_full_unstemmed | Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin |
| title_short | Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin |
| title_sort | nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / ahmad ihsan mohd yassin |
| topic | Malaysia |
| url | https://ir.uitm.edu.my/id/eprint/19441/ |