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Algorithms 2016, 9(3), 49; doi:10.3390/a9030049

Data Filtering Based Recursive and Iterative Least Squares Algorithms for Parameter Estimation of Multi-Input Output Systems

1
Department of Mathematics, Jining University, Qufu 273155, China
2
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Academic Editor: Natarajan Meghanathan
Received: 24 April 2016 / Revised: 19 July 2016 / Accepted: 20 July 2016 / Published: 26 July 2016
(This article belongs to the Special Issue Algorithms for Complex Network Analysis)
View Full-Text   |   Download PDF [264 KB, uploaded 26 July 2016]   |  

Abstract

This paper discusses the parameter estimation problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea and the data filtering technique, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm and a data filtering based iterative least squares (F-LSI) identification algorithm are derived. Compared with the F-RGLS algorithm, the proposed F-LSI algorithm is more effective and can generate more accurate parameter estimates. The simulation results confirm this conclusion. View Full-Text
Keywords: multivariable system; filtering technique; iterative identification; recursive least squares multivariable system; filtering technique; iterative identification; recursive least squares
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ding, J. Data Filtering Based Recursive and Iterative Least Squares Algorithms for Parameter Estimation of Multi-Input Output Systems. Algorithms 2016, 9, 49.

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