Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems

Bingbing Shen; Feng Ding; Ling Xu; Tasawar Hayat

doi:10.1007/s12555-017-0596-y

Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems

Bingbing Shen
, Feng Ding
, Ling Xu
, Tasawar Hayat

Jiangnan University
Qingdao University of Science and Technology
Faculty of Sciences, King Abdulaziz University

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

21 Scopus citations

Abstract

With the development of industry information technology, many researchers pay attention to the estimation problems of feedback nonlinear systems increasingly. In this paper, a filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein equation-error autoregressive systems by using the hierarchical technique. The parameter estimates accuracy can be improved with the innovation length increasing. These algorithms are easy to implement on-line. The simulation results verify the effectiveness of the proposed algorithm.

Original language	English
Pages (from-to)	2225-2234
Number of pages	10
Journal	International Journal of Control, Automation and Systems
Volume	16
Issue number	5
DOIs	https://doi.org/10.1007/s12555-017-0596-y
State	Published - 1 Oct 2018
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Feedback nonlinear system
filtering
gradient search
multi-innovation identification
parameter estimation

Access to Document

10.1007/s12555-017-0596-y

Cite this

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title = "Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems",

abstract = "With the development of industry information technology, many researchers pay attention to the estimation problems of feedback nonlinear systems increasingly. In this paper, a filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein equation-error autoregressive systems by using the hierarchical technique. The parameter estimates accuracy can be improved with the innovation length increasing. These algorithms are easy to implement on-line. The simulation results verify the effectiveness of the proposed algorithm.",

keywords = "Feedback nonlinear system, filtering, gradient search, multi-innovation identification, parameter estimation",

author = "Bingbing Shen and Feng Ding and Ling Xu and Tasawar Hayat",

note = "Publisher Copyright: {\textcopyright} 2018, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.",

year = "2018",

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T1 - Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems

AU - Shen, Bingbing

AU - Ding, Feng

AU - Xu, Ling

AU - Hayat, Tasawar

PY - 2018/10/1

Y1 - 2018/10/1

N2 - With the development of industry information technology, many researchers pay attention to the estimation problems of feedback nonlinear systems increasingly. In this paper, a filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein equation-error autoregressive systems by using the hierarchical technique. The parameter estimates accuracy can be improved with the innovation length increasing. These algorithms are easy to implement on-line. The simulation results verify the effectiveness of the proposed algorithm.

AB - With the development of industry information technology, many researchers pay attention to the estimation problems of feedback nonlinear systems increasingly. In this paper, a filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein equation-error autoregressive systems by using the hierarchical technique. The parameter estimates accuracy can be improved with the innovation length increasing. These algorithms are easy to implement on-line. The simulation results verify the effectiveness of the proposed algorithm.

KW - Feedback nonlinear system

KW - filtering

KW - gradient search

KW - multi-innovation identification

KW - parameter estimation

UR - https://www.scopus.com/pages/publications/85053466243

U2 - 10.1007/s12555-017-0596-y

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JO - International Journal of Control, Automation and Systems

JF - International Journal of Control, Automation and Systems

IS - 5

ER -

Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems

Abstract

UN SDGs

Keywords

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