Coupled stochastic gradient identification algorithms for multivariate output-error systems using the auxiliary model

Wu Huang; Feng Ding; Tasawar Hayat; Ahmed Alsaedi

doi:10.1007/s12555-016-0454-3

Coupled stochastic gradient identification algorithms for multivariate output-error systems using the auxiliary model

Wu Huang
, Feng Ding
, Tasawar Hayat
, Ahmed Alsaedi

Jiangnan University
Faculty of Sciences, King Abdulaziz University
Quaid-I-Azam University

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

This paper considers the gradient based identification problem of a multivariate output-error system. By using the auxiliary model identification idea and the coupling identification concept, an auxiliary model based stochastic gradient (AM-SG) algorithm and a coupled AM-SG algorithm are presented. The results indicate that the parameter estimation errors converge to zero under the persistent excitation conditions. The simulation examples confirm the theoretical results.

Original language	English
Pages (from-to)	1622-1631
Number of pages	10
Journal	International Journal of Control, Automation and Systems
Volume	15
Issue number	4
DOIs	https://doi.org/10.1007/s12555-016-0454-3
State	Published - 1 Aug 2017
Externally published	Yes

Keywords

Auxiliary model
coupling identification
gradient search
multivariate system
parameter estimation

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10.1007/s12555-016-0454-3

Cite this

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title = "Coupled stochastic gradient identification algorithms for multivariate output-error systems using the auxiliary model",

abstract = "This paper considers the gradient based identification problem of a multivariate output-error system. By using the auxiliary model identification idea and the coupling identification concept, an auxiliary model based stochastic gradient (AM-SG) algorithm and a coupled AM-SG algorithm are presented. The results indicate that the parameter estimation errors converge to zero under the persistent excitation conditions. The simulation examples confirm the theoretical results.",

keywords = "Auxiliary model, coupling identification, gradient search, multivariate system, parameter estimation",

author = "Wu Huang and Feng Ding and Tasawar Hayat and Ahmed Alsaedi",

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AU - Huang, Wu

AU - Ding, Feng

AU - Hayat, Tasawar

AU - Alsaedi, Ahmed

PY - 2017/8/1

Y1 - 2017/8/1

N2 - This paper considers the gradient based identification problem of a multivariate output-error system. By using the auxiliary model identification idea and the coupling identification concept, an auxiliary model based stochastic gradient (AM-SG) algorithm and a coupled AM-SG algorithm are presented. The results indicate that the parameter estimation errors converge to zero under the persistent excitation conditions. The simulation examples confirm the theoretical results.

AB - This paper considers the gradient based identification problem of a multivariate output-error system. By using the auxiliary model identification idea and the coupling identification concept, an auxiliary model based stochastic gradient (AM-SG) algorithm and a coupled AM-SG algorithm are presented. The results indicate that the parameter estimation errors converge to zero under the persistent excitation conditions. The simulation examples confirm the theoretical results.

KW - Auxiliary model

KW - coupling identification

KW - gradient search

KW - multivariate system

KW - parameter estimation

UR - https://www.scopus.com/pages/publications/85025130802

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JO - International Journal of Control, Automation and Systems

JF - International Journal of Control, Automation and Systems

IS - 4

ER -

Coupled stochastic gradient identification algorithms for multivariate output-error systems using the auxiliary model

Abstract

Keywords

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