Auxiliary model based recursive generalized least squares identification algorithm for multivariate output-error autoregressive systems using the decomposition technique

Qinyao Liu, Feng Ding*, Yan Wang, Cheng Wang, Tasawar Hayat

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the decomposition technique and the auxiliary model identification idea, we derive a decomposition based auxiliary model recursive generalized least squares algorithm. The key is to divide the system into two fictitious subsystems, the one including a parameter vector and the other including a parameter matrix, and to estimate the two subsystems using the recursive least squares method, respectively. Compared with the auxiliary model based recursive generalized least squares algorithm, the proposed algorithm has less computational burden. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms.

Original languageEnglish
Pages (from-to)7643-7663
Number of pages21
JournalJournal of the Franklin Institute
Volume355
Issue number15
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Fingerprint

Dive into the research topics of 'Auxiliary model based recursive generalized least squares identification algorithm for multivariate output-error autoregressive systems using the decomposition technique'. Together they form a unique fingerprint.

Cite this