System Identification using orthonormal basis filter
Dynamic models play key roles in model predictive control (MPC), fault tolerant control system and other model based control system. The process of developing model from experimental data is known as system identification. The widely used dynamic models for identification of linear time invariant sy...
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my.utp.eprints.18722012-12-31T03:48:35Z System Identification using orthonormal basis filter Tufa , L.D. Ramasamy , Marappagounder Mahadzir, Shuhaimi TP Chemical technology Dynamic models play key roles in model predictive control (MPC), fault tolerant control system and other model based control system. The process of developing model from experimental data is known as system identification. The widely used dynamic models for identification of linear time invariant systems in process industries are Auto Regressive with Exogenous inputs (ARX) and Finite Impulse Response (FIR). Their popularity is due to their simplicity in developing the model. However, they need very large amount of data to reduce variance error. In addition, ordinary ARX model structures lead to inconsistent model parameters. Orthonormal Basis Filter (OBF) model stuctures permit incorporation of prior knowledge of the system in the form of one or more poles, which renders it the capacity to capture the system dynamics with a few numbers of parameters (parsimonous in parameters). In addition the resulting OBF models are consistent in parameters. 2009 Conference or Workshop Item PeerReviewed image/jpeg http://eprints.utp.edu.my/1872/1/ICCBPE2009A.jpg Tufa , L.D. and Ramasamy , Marappagounder and Mahadzir, Shuhaimi (2009) System Identification using orthonormal basis filter. In: 3rd International Conference on Chemical &Bioprocess Eng in conjunction with 23rd Symposium of Malaysian Chemical Engineers, 12 - 14 August 2009, Kota Kinabalu Sabah. http://eprints.utp.edu.my/1872/ |
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TP Chemical technology Tufa , L.D. Ramasamy , Marappagounder Mahadzir, Shuhaimi System Identification using orthonormal basis filter |
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Dynamic models play key roles in model predictive control (MPC), fault tolerant control system and other model based control system. The process of developing model from experimental data is known as system identification. The widely used dynamic models for identification of linear time invariant systems in process industries are Auto Regressive with Exogenous inputs (ARX) and Finite Impulse Response (FIR). Their popularity is due to their simplicity in developing the model. However, they need very large amount of data to reduce variance error. In addition, ordinary ARX model structures lead to inconsistent model parameters. Orthonormal Basis Filter (OBF) model stuctures permit incorporation of prior knowledge of the system in the form of one or more poles, which renders it the capacity to capture the system dynamics with a few numbers of parameters (parsimonous in parameters). In addition the resulting OBF models are consistent in parameters. |
format |
Conference or Workshop Item |
author |
Tufa , L.D. Ramasamy , Marappagounder Mahadzir, Shuhaimi |
author_facet |
Tufa , L.D. Ramasamy , Marappagounder Mahadzir, Shuhaimi |
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Tufa , L.D. |
title |
System Identification using orthonormal basis filter |
title_short |
System Identification using orthonormal basis filter |
title_full |
System Identification using orthonormal basis filter |
title_fullStr |
System Identification using orthonormal basis filter |
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System Identification using orthonormal basis filter |
title_sort |
system identification using orthonormal basis filter |
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2009 |
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http://eprints.utp.edu.my/1872/1/ICCBPE2009A.jpg http://eprints.utp.edu.my/1872/ |
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