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Performance Comparison of Perturbation Signals for Time-Varying Water Temperature Modeling Using NARX-Based BPSO

  • Najidah Hambali
  • Mohd Nasir Taib
  • Ahmad Ihsan Mohd Yassin
  • Mohd Hezri Fazalul RahimanEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 538)

Abstract

There is an increasing concern on the perturbation signal analysis on nonlinear modeling for nonlinear systems. Several studies have shown the importance of suitable perturbation signal for the real nonlinear system applications. This study systematically reviews the performance comparison for nonlinear modeling using two perturbation signals, namely as Pseudo Random Binary Signal (PRBS) and Multi-level Pseudo Random Sequence (MPRS) for a time-varying water temperature of Steam Distillation Pilot Plant (SDPP). A Binary Particle Swarm Optimization (BPSO) algorithm was utilized in the model structure selection for polynomial Nonlinear Auto-Regressive with eXogenous (NARX) input. Three model’s selection criteria were examined; Akaike Information Criterion (AIC), Model Descriptor Length (MDL), and Final Prediction Error (FPE) for performance analysis that included model validation. The results presented lesser number of input and output lags, also fewer output model parameters for MPRS perturbation signal. Further analysis of the nonlinear model has demonstrated high R-squared and low MSE for model validation for both models using PRBS and MPRS perturbation signals.

Keywords

System identification Time-varying temperature Nonlinear model Particle swarm optimization Distillation column Perturbation signal 

Notes

Acknowledgements

This project was funded by Institute of Research Management & Innovation (IRMI), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia [grant number 600-IRMI/DANA 5/3 BESTARI 9049/2017)] and Kementerian Pendidikan Tinggi Malaysia [SLAI Scholarship] for the first author. The first author would like to thank Jabatan Pengurusan Sumber Manusia (JPSM), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia for the scholarship management.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Najidah Hambali
    • 1
  • Mohd Nasir Taib
    • 1
  • Ahmad Ihsan Mohd Yassin
    • 1
  • Mohd Hezri Fazalul Rahiman
    • 1
    Email author
  1. 1.Faculty of Electrical EngineeringUniversiti Teknologi MARAShah AlamMalaysia

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