Evidence map›Paper›PMID 30110418›Full record

ArticleRoyal Society open science2018

Separate block-based parameter estimation method for Hammerstein systems.

Shuo Zhang, Dongqing Wang, Feng Liu

Abstract read
In one paragraph

Article in Royal Society open science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shuo ZhangCollege of Automation and Electrical Engineering, Qingdao University, Qingdao, 266071, People's Republic of China.
Dongqing WangCollege of Automation and Electrical Engineering, Qingdao University, Qingdao, 266071, People's Republic of China.ORCID 0000-0001-8856-4289
Feng LiuDepartment of Industrial Engineering, University of Texas at Arlington, TX 76019, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Hammerstein systemsleast squaresparameter estimationseparate block

Identifiers

PMID30110418
PMCPMC6030268

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.