Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition

Junxia Ma, Feng Ding, Erfu Yang

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorithm is presented for estimating the parameter vector in each subsystem. Moreover, a data filtering-based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the data filtering-based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm.

LanguageEnglish
Pages1895-1908
Number of pages14
JournalNonlinear Dynamics
Volume83
Issue number4
Early online date29 Oct 2015
DOIs
Publication statusPublished - 1 Mar 2016

Fingerprint

Least Square Algorithm
Iterative Algorithm
Nonlinear systems
Filtering
Nonlinear Systems
Decomposition
Decompose
Subsystem
Identification Problem
Model
Identification (control systems)
Estimate
Simulation

Keywords

  • data filtering
  • iterative algorithm
  • least squares
  • model decomposition
  • nonlinear system

Cite this

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Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition. / Ma, Junxia; Ding, Feng; Yang, Erfu.

In: Nonlinear Dynamics, Vol. 83, No. 4, 01.03.2016, p. 1895-1908.

Research output: Contribution to journalArticle

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