Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

Liu, Qinyao and Ding, Feng and Yang, Erfu (2018) Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems. Digital Signal Processing: A Review Journal, 83. pp. 323-331. ISSN 1051-2004

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Abstract

This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm.

ORCID iDs

Ding, Feng ORCID: https://orcid.org/0000-0002-9787-4171

Yang, Erfu ORCID: https://orcid.org/0000-0003-1813-5950

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  Item type:	Article
  ID code:	72121
  Dates:	Date Event 31 December 2018 Published 25 September 2018 Published Online 16 September 2018 Accepted
  Keywords:	auxiliary model, coupling identification, multivariable system, parameter estimation, stochastic gradient, Electrical engineering. Electronics Nuclear engineering, Signal Processing, Computer Vision and Pattern Recognition, Statistics, Probability and Uncertainty, Computational Theory and Mathematics, Electrical and Electronic Engineering, Artificial Intelligence, Applied Mathematics
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering Faculty of Engineering > Design, Manufacture and Engineering Management
  Depositing user:	Pure Administrator
  Date deposited:	21 Apr 2020 13:29
  Last modified:	03 Apr 2021 00:42
  Related URLs:	Scopus publication
  URI:	https://strathprints.strath.ac.uk/id/eprint/72121