NDLI: Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

Content Provider	IET Digital Library
Author	Ma, Hao Pan, Jian Ding, Feng Xu, Ling Ding, Wenfang
Abstract	This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.
Starting Page	3040
Ending Page	3051
Page Count	12
ISSN	17518644
Volume Number	13
e-ISSN	17518652
Issue Number	Issue 18, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/13/18
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2019.0112
Journal	IET Control Theory & Applications
Publisher Date	2019-09-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autoregressive Moving Average Noise Autoregressive Moving Average Processes Gradient Method Identification Problem Information Vector Interpolation And Function Approximation Iterative Method Iterative Parameter Estimation Iterative Search Least Squares Approximation Modelling And Identification Multivariable Output-error-like Autoregressive Moving Average System Multivariable Output-error-like System Numerical Analysis Parameter Estimation Parameter Vector Recursive Least Squares Algorithm Redundant Parameter Estimate Simulation Statistics Unknown Variables Vector
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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