Gradient-Based Recursive Identification Methods for Input Nonlinear Equation Error Closed-Loop Systems

Bingbing Shen; Feng Ding; Ahmed Alsaedi; Tasawar Hayat

doi:10.1007/s00034-016-0394-4

Gradient-Based Recursive Identification Methods for Input Nonlinear Equation Error Closed-Loop Systems

Bingbing Shen
, Feng Ding
, Ahmed Alsaedi
, Tasawar Hayat

Jiangnan University
King Abdulaziz University
Quaid-I-Azam University

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

10 Scopus citations

Abstract

The identification problem of closed-loop or feedback nonlinear systems is a hot topic. Based on the hierarchical identification principle, this paper presents a hierarchical stochastic gradient algorithm and a hierarchical multi-innovation stochastic gradient algorithm for feedback nonlinear systems. The simulation results show that the hierarchical multi-innovation stochastic gradient can more effectively estimate the parameters of the feedback nonlinear systems than the hierarchical stochastic gradient algorithm.

Original language	English
Pages (from-to)	2166-2183
Number of pages	18
Journal	Circuits, Systems, and Signal Processing
Volume	36
Issue number	5
DOIs	https://doi.org/10.1007/s00034-016-0394-4
State	Published - 1 May 2017
Externally published	Yes

Keywords

Closed-loop system
Hierarchical identification
Multi-innovation
Nonlinear system
Parameter estimation
Stochastic gradient

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10.1007/s00034-016-0394-4

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title = "Gradient-Based Recursive Identification Methods for Input Nonlinear Equation Error Closed-Loop Systems",

abstract = "The identification problem of closed-loop or feedback nonlinear systems is a hot topic. Based on the hierarchical identification principle, this paper presents a hierarchical stochastic gradient algorithm and a hierarchical multi-innovation stochastic gradient algorithm for feedback nonlinear systems. The simulation results show that the hierarchical multi-innovation stochastic gradient can more effectively estimate the parameters of the feedback nonlinear systems than the hierarchical stochastic gradient algorithm.",

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author = "Bingbing Shen and Feng Ding and Ahmed Alsaedi and Tasawar Hayat",

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AU - Shen, Bingbing

AU - Ding, Feng

AU - Alsaedi, Ahmed

AU - Hayat, Tasawar

PY - 2017/5/1

Y1 - 2017/5/1

N2 - The identification problem of closed-loop or feedback nonlinear systems is a hot topic. Based on the hierarchical identification principle, this paper presents a hierarchical stochastic gradient algorithm and a hierarchical multi-innovation stochastic gradient algorithm for feedback nonlinear systems. The simulation results show that the hierarchical multi-innovation stochastic gradient can more effectively estimate the parameters of the feedback nonlinear systems than the hierarchical stochastic gradient algorithm.

AB - The identification problem of closed-loop or feedback nonlinear systems is a hot topic. Based on the hierarchical identification principle, this paper presents a hierarchical stochastic gradient algorithm and a hierarchical multi-innovation stochastic gradient algorithm for feedback nonlinear systems. The simulation results show that the hierarchical multi-innovation stochastic gradient can more effectively estimate the parameters of the feedback nonlinear systems than the hierarchical stochastic gradient algorithm.

KW - Closed-loop system

KW - Hierarchical identification

KW - Multi-innovation

KW - Nonlinear system

KW - Parameter estimation

KW - Stochastic gradient

UR - https://www.scopus.com/pages/publications/85015446156

U2 - 10.1007/s00034-016-0394-4

DO - 10.1007/s00034-016-0394-4

M3 - Article

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JO - Circuits, Systems, and Signal Processing

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IS - 5

ER -

Gradient-Based Recursive Identification Methods for Input Nonlinear Equation Error Closed-Loop Systems

Abstract

Keywords

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