Separate block-based parameter estimation method for Hammerstein systems

Different from the output–input representation-based identification methods of two-block Hammerstein systems, this paper concerns a separate block-based parameter estimation method for each block of a two-block Hammerstein CARMA system, without combining the parameters of two parts together. The ide...

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Bibliographic Details
Published in:Royal Society open science Vol. 5; no. 6; p. 172194
Main Authors: Zhang, Shuo, Wang, Dongqing, Liu, Feng
Format: Journal Article
Language:English
Published: England The Royal Society Publishing 01.06.2018
The Royal Society
Subjects:
Computer Science
Hammerstein Systems
Least Squares
Parameter Estimation
Separate Block
parameter estimation
least squares
separate block
Hammerstein systems
ISSN:2054-5703, 2054-5703
Online Access:Get full text
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Summary:Different from the output–input representation-based identification methods of two-block Hammerstein systems, this paper concerns a separate block-based parameter estimation method for each block of a two-block Hammerstein CARMA system, without combining the parameters of two parts together. The idea is to consider each block as a subsystem and to estimate the parameters of the nonlinear block and the linear block separately (interactively), by using two least-squares algorithms in one recursive step. The internal variable between the two blocks (the output of the nonlinear block, and also the input of the linear block) is replaced by different estimates: when estimating the parameters of the nonlinear part, the internal variable between the two blocks is computed by the linear function; when estimating the parameters of the linear part, the internal variable is computed by the nonlinear function. The proposed parameter estimation method possesses property of the higher computational efficiency compared with the previous over-parametrization method in which many redundant parameters need to be computed. The simulation results show the effectiveness of the proposed algorithm.
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ISSN:2054-5703
2054-5703
DOI:10.1098/rsos.172194