WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification
type: Item
instance of: scholarly article
title: WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification
zbMATH Open document ID: 1398.93351
DOI: 10.1155/2018/1753262
author: G. Richomme
published in: Complexity
publication date: 2018-09-26T00:00:00Z
review text: Summary: Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modeling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analyzed by using a demonstration example and a nonlinear system identification benchmark.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93D25
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6944131
zbMATH Keywords: Wiener-Hammerstein systems
zbMATH Keywords: evolutionary algorithm
zbMATH Keywords: Wiener-Hammerstein system identification
zbMATH Keywords: best linear approximation (BLA)
Wikidata QID: Q59202370
describes a project that uses: Matlab
describes a project that uses: System Identification Toolbox
describes a project that uses: Daisy
describes a project that uses: Wiener-Hammerstein Benchmark
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/1753262
OpenAlex ID: W2788313192
cites work: Q4003224
cites work: Q5413441
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DBLP publication ID: journals/complexity/ZambranoSDM18
description: scientific article; zbMATH DE number 6944131