System identification of essential oil extraction system using non-linear autoregressive model with exogenous inputs (NARX) / Farahida Awadz
This project explores the application of system identification using Non-Linear Autoregressive Model with Exogeneous Inputs (NARX) of an essential oil extraction system. Model structure selection was performed by applying the Binary Particle Swarm Optimization (BPSO) algorithm which been developed b...
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| Format: | Thesis |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/77928/1/77928.pdf |
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| Summary: | This project explores the application of system identification using Non-Linear Autoregressive Model with Exogeneous Inputs (NARX) of an essential oil extraction system. Model structure selection was performed by applying the Binary Particle Swarm Optimization (BPSO) algorithm which been developed by (J.Kennedy and R.Eberhart, 1997). The application of BPSO for model structure selection can be described by representing each particle's position in binary value. Then, the binary value is used to select a set of regressors from the regressor matrix. The QR factorization is used to estimate the parameters of the reduced regressor matrix. The tests that been performed based on the essential oil extraction system by (Rahiman, 2009), show that the BPSO has the potential to become an effective method to determine the NARX model structure in the system identification model. |
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