System identification of PFC rectifier controller using non-linear autoregressive moving average with exogenous inputs (NARMAX) model / Mohd Benyamin Sabtu
In this project, the model structure selection of a Non-Linear Autoregressive Moving Average with Exogenous Input (NARMAX) identification of a Power Factor Correction (PFC) Rectifier Controller was performed by applying the Orthogonal Least Square (OLS) algorithm. The NARMAX model was introduced by...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2010
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| الوصول للمادة أونلاين: | https://ir.uitm.edu.my/id/eprint/84824/1/84824.pdf |
| الوسوم: |
إضافة وسم
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| الملخص: | In this project, the model structure selection of a Non-Linear Autoregressive Moving Average with Exogenous Input (NARMAX) identification of a Power Factor Correction (PFC) Rectifier Controller was performed by applying the Orthogonal Least Square (OLS) algorithm. The NARMAX model was introduced by Leontaritis and Billings (1985). The OLS estimation algorithm has been found to be an efficient tool for the estimation of non-linear systems. The tests that been performed based on the PFC Rectifier Controller dataset, show that the OLS has the potential to become an effective method to determine the NARMAX model structure in the system identification model. |
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