Filtering based multi-stage recursive least squares parameter estimation algorithm for input nonlinear output-error autoregressive systems

A filtering based multi-stage recursive estimation method is presented in this article. The system to be identified is called Hammerstein model, in which the output is described by a pseudo-linear regressive form of all unknown parameters based on the key term separation. Filtering the input and out...

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Bibliographic Details
Published in:Chinese Control Conference pp. 1921 - 1925
Main Authors: Ma, Junxia, Chen, Jing, Ding, Feng
Format: Conference Proceeding Journal Article
Language:English
Published: TCCT 01.07.2016
Subjects:
Algorithms
Data models
Decomposition
Filtering
Filtration
Heuristic algorithms
Least squares method
Mathematical analysis
Mathematical model
Mathematical models
Nonlinear model
Nonlinear systems
Numerical models
Parameter estimation
Parameter identification
Vectors (mathematics)
ISSN:1934-1768
Online Access:Get full text
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Summary:A filtering based multi-stage recursive estimation method is presented in this article. The system to be identified is called Hammerstein model, in which the output is described by a pseudo-linear regressive form of all unknown parameters based on the key term separation. Filtering the input and output data and separating the original unknown parameter vector into a few low-dimensional vectors, then interactively identifying each of the vectors is the basic thought of the proposed algorithm. Because the dimensions of the involved covariance matrices are smaller than those in the recursive generalized least squares algorithm, the discussed method has a lower calculational burden. The numerical experiment results demonstrate the validity of the presented method.
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SourceType-Conference Papers & Proceedings-2
ISSN:1934-1768
DOI:10.1109/ChiCC.2016.7553375