Data filtering based recursive least squares algorithm for two-input single-output systems with moving average noises
type: Item
instance of: scholarly article
title: Data filtering based recursive least squares algorithm for two-input single-output systems with moving average noises
zbMATH Open document ID: 1406.93359
DOI: 10.1155/2014/694053
author: Xianling Lu
author: Wei Zhou
author: Wenlin Shi
published in: Journal of Applied Mathematics
publication date: 2019-02-01T00:00:00Z
review text: Summary: This paper studies identification problems of two-input single-output controlled autoregressive moving average systems by using an estimated noise transfer function to filter the input-output data. Through data filtering, we obtain two simple identification models, one containing the parameters of the system model and the other containing the parameters of the noise model. Furthermore, we deduce a data filtering based recursive least squares method for estimating the parameters of these two identification models, respectively, by replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of its covariance matrices become small. The simulation results indicate that the proposed algorithm is effective.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 7010714
Wikidata QID: Q59054078
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/694053
OpenAlex ID: W2043815043
DBLP publication ID: journals/jam/LuZS14
description: scientific article; zbMATH DE number 7010714