NDLI: Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique

Content Provider	IET Digital Library
Author	Liu, Qinyao Ding, Feng Xu, Ling Yang, Erfu
Abstract	System identification provides many convenient and useful methods for engineering modelling. This study targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the coloured noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then the filtered system is decomposed into several subsystems and a filtering-based partially-coupled generalised extended stochastic gradient algorithm is developed via the coupling concept. In contrast to the multivariable generalised extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples.
Starting Page	642
Ending Page	650
Page Count	9
ISSN	17518644
Volume Number	13
e-ISSN	17518652
Issue Number	Issue 5, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2018.5541
Journal	IET Control Theory & Applications
Publisher Date	2019-01-23
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Parameter Estimate Autoregressive Moving Average Processes Autoregressive Processes Average System Coloured Noises Convenient Method Data Filtering Technique Engineering Modelling Filtered System Filtering Method in Signal Processing Filtering-based Gradient Method Input Interpolation And Function Approximation Least Squares Approximation Modelling And Identification Multivariable Equation-error Autoregressive Multivariable Generalised Extended Stochastic Gradient Algorithm Numerical Analysis Output Data Parameter Estimation Parameter Identification Problem Simulation Statistics Stochastic Linearised SCUC System Identification Useful Method White Noise White Noises
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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