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A review on type-2 fuzzy neural networks for system identification
In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-N...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941344/ https://www.ncbi.nlm.nih.gov/pubmed/33716561 http://dx.doi.org/10.1007/s00500-021-05686-5 |
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author | Tavoosi, Jafar Mohammadzadeh, Ardashir Jermsittiparsert, Kittisak |
author_facet | Tavoosi, Jafar Mohammadzadeh, Ardashir Jermsittiparsert, Kittisak |
author_sort | Tavoosi, Jafar |
collection | PubMed |
description | In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges. |
format | Online Article Text |
id | pubmed-7941344 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-79413442021-03-09 A review on type-2 fuzzy neural networks for system identification Tavoosi, Jafar Mohammadzadeh, Ardashir Jermsittiparsert, Kittisak Soft comput Methodologies and Application In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges. Springer Berlin Heidelberg 2021-03-09 2021 /pmc/articles/PMC7941344/ /pubmed/33716561 http://dx.doi.org/10.1007/s00500-021-05686-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Methodologies and Application Tavoosi, Jafar Mohammadzadeh, Ardashir Jermsittiparsert, Kittisak A review on type-2 fuzzy neural networks for system identification |
title | A review on type-2 fuzzy neural networks for system identification |
title_full | A review on type-2 fuzzy neural networks for system identification |
title_fullStr | A review on type-2 fuzzy neural networks for system identification |
title_full_unstemmed | A review on type-2 fuzzy neural networks for system identification |
title_short | A review on type-2 fuzzy neural networks for system identification |
title_sort | review on type-2 fuzzy neural networks for system identification |
topic | Methodologies and Application |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941344/ https://www.ncbi.nlm.nih.gov/pubmed/33716561 http://dx.doi.org/10.1007/s00500-021-05686-5 |
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