Hierarchical algorithm for the identification of parameter estimation of linear system

Mohammad Abu Jami'in, Khairul Anam, Riries Rulaningtyas, Mohammaderik Echsony

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

A novel technique to identification of autoregressive moving average (ARMA)systems is proposed to increase the accuracy and speed of convergence for the system identification. The convergence speed of recursive least square algorithm (RLS) is solved under differential equations that needs all necessary information about the asymptotic behavior. Using RLS estimation, the convergence of parameters is able to the true values if the data of information vector growing to infinite. Therefore, the convergence of the parameters of the RLS algorithm takes time or needs a large number of sampling. In order to improve the accuracy and convergence speed of the estimated parameters, we propose a technique that modifies the QARXNN model by running two steps to identify the system hierarchically. The proposed method performs two steps: first, the system is identified by least square error (LSE) algorithm. Second, performs multi-input multi-output feedforward neural networks (MIMO-NN) to refine the estimated parameters by updating the parameters based on the residual error of LSE. The residual error by using LSE is set as target output to train NN. Finally, we illustrate and verify the proposed technique with an experimental studies. The proposed method can find the estimated parameters faster with = 0.935129 % in tenth sampling. The results is almost consistence which the accuracy of the identified parameters did not change significantly with the increasing number of sampling or the number of data points.

Original languageEnglish
Title of host publication3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages71-76
Number of pages6
ISBN (Electronic)9781538674079
DOIs
Publication statusPublished - 2 Jul 2018
Event3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Malang, Indonesia
Duration: 10 Nov 201812 Nov 2018

Publication series

Name3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings

Conference

Conference3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018
Country/TerritoryIndonesia
CityMalang
Period10/11/1812/11/18

Keywords

  • System identification
  • convergence speed
  • hierarchical algorithm
  • parameter estimation
  • quasi-linear ARX neural network

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