research-article

Recursive Identification for Hammerstein-Wiener system based on extreme learning machine

Authors:

Zhenzhen Han,

Yunli Wang,

LuYang ZhangAuthors Info & Claims

ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

Article No.: 20, Pages 1 - 4

https://doi.org/10.1145/3469213.3470220

Published: 18 August 2021 Publication History

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Abstract

This paper focus on identifying Hammerstein-Wiener (H-W) model, where the model employs two different ELM networks as nonlinear blocks, respectively. In the model, the orders of dynamic linear part are known. Through some transformations, the output of model becomes linear with respect to unknown parameters. Therefore, the variable forgetting factor recursive least squares algorithm is presented for estimating the parameters of the H-W model. Simulation on temperature control model of water bath is presented to show efficiency of ELM-based H-W model and the recursive identification method.

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Highlights
- Extreme learning machine algorithm is applied to estimate Hammerstein-Wiener model.
Abstract
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ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

May 2021

2053 pages

ISBN:9781450390200

DOI:10.1145/3469213

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 18 August 2021

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ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

May 28 - 30, 2021

Chongqing, China

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Linear approximation and identification of MIMO Wiener-Hammerstein systems

Wiener and Hammerstein uncertain models identification

A novel extreme learning Machine-based Hammerstein-Wiener model for complex nonlinear industrial processes

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