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

Jiling Ding

doi:10.3390/a9030049

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.

Highlights

IntroductionSystem modeling and identification of single variable processes have been well studied
System modeling and identification of single variable processes have been well studied.most industrial processes are multivariable systems [1,2,3], including multiple-input multiple-output (MIMO) systems and multiple-input single-output (MISO) systems
By using the data filtering technique and the auxiliary model identification idea, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm is derived for the multi-input output-error autoregressive (OEAR) system

Summary

Introduction

System modeling and identification of single variable processes have been well studied. In the field of system identification, the filtering technique is efficient to improve the computational efficiency [20,21,22], and it has been widely used in parameter estimation of different models [23,24]. This paper combines the filtering technique with the auxiliary model identification idea to estimate parameters of multi-input output error autoregressive (OEAR) systems. By using the data filtering technique and the auxiliary model identification idea, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm is derived for the multi-input OEAR system. A data filtering based iterative least squares (F-LSI) identification algorithm is developed for the multi-input OEAR system.

The System Description

The Data Filtering Based Recursive Least Squares Algorithm

The Data Filtering Based Iterative Least Squares Algorithm

Examples

Conclusions