Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

Qinyao Liu, Feng Ding, Erfu Yang

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)323-331
Number of pages9
JournalDigital Signal Processing: A Review Journal
Volume83
Early online date25 Sep 2018
DOIs
Publication statusPublished - 31 Dec 2018

Keywords

  • auxiliary model
  • coupling identification
  • multivariable system
  • parameter estimation
  • stochastic gradient

Fingerprint Dive into the research topics of 'Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems'. Together they form a unique fingerprint.

Cite this