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Journal of Applied Sciences
  Year: 2014 | Volume: 14 | Issue: 15 | Page No.: 1711-1719
DOI: 10.3923/jas.2014.1711.1719
 
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Comparison between ARX and FIR Decorrelation Models in Detecting Model-plant Mismatch

Nur Hidayah Kamal Iqbal, Nooryusmiza Yusoff and Lemma Dendena Tufa

Abstract:
Partial correlation analysis is used in detecting the model-plant mismatch as it will give accurate location of mismatched submodel. In the decorrelation step of the observed input and model residual from the other affecting inputs, model is identified to capture the dynamic relationship of the input-output data. In this study, the use of Auto Regressive with Exogenous Inputs (ARX) and Finite Impulse Response (FIR) model structures are compared to investigate the effectiveness of detecting the gain mismatch using different model structures. The optimal numbers of parameters of the estimated models also plays a significant role in the detection of mismatched submodel. In this research, the use of the ARX model structure outperforms the FIR model structure in detecting the single submodel mismatch which successfully demonstrated to the process system of Wood and Berry distillation column.
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How to cite this article:

Nur Hidayah Kamal Iqbal, Nooryusmiza Yusoff and Lemma Dendena Tufa, 2014. Comparison between ARX and FIR Decorrelation Models in Detecting Model-plant Mismatch. Journal of Applied Sciences, 14: 1711-1719.

DOI: 10.3923/jas.2014.1711.1719

URL: https://scialert.net/abstract/?doi=jas.2014.1711.1719

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