Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature

This paper presents a review of the central theories involved in hybrid models based on fuzzy systems and artificial neural networks, mainly focused on supervised methods for training hybrid models. The basic concepts regarding the history of hybrid models, from the first proposed model to the curre...

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Published inApplied soft computing Vol. 92; p. 106275
Main Author de Campos Souza, Paulo Vitor
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2020
Subjects
Fuzzy neural network
Hybrid models
Neuro-fuzzy network
Hybrid models
Neuro-fuzzy network
Fuzzy neural network
Online AccessGet full text
ISSN1568-4946
1872-9681
DOI10.1016/j.asoc.2020.106275

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Abstract This paper presents a review of the central theories involved in hybrid models based on fuzzy systems and artificial neural networks, mainly focused on supervised methods for training hybrid models. The basic concepts regarding the history of hybrid models, from the first proposed model to the current advances, the composition and the functionalities in their architecture, the data treatment and the training methods of these intelligent models are presented to the reader so that the evolution of this category of intelligent systems can be evidenced. Finally, the features of the leading models and their applications are presented to the reader. We conclude that the fuzzy neural network models and their derivations are efficient in constructing a system with a high degree of accuracy and an appropriate level of interpretability working in a wide range of areas of economics and science. •An extensive review of the major aspects of fuzzy neural networks and neuro-fuzzy networks.•Approaches to related work in the literature and history of hybrid models.•Presentation of features and techniques involved in the construction of hybrid models.•Presentation of practical approaches of hybrid models in several applied contexts.
AbstractList This paper presents a review of the central theories involved in hybrid models based on fuzzy systems and artificial neural networks, mainly focused on supervised methods for training hybrid models. The basic concepts regarding the history of hybrid models, from the first proposed model to the current advances, the composition and the functionalities in their architecture, the data treatment and the training methods of these intelligent models are presented to the reader so that the evolution of this category of intelligent systems can be evidenced. Finally, the features of the leading models and their applications are presented to the reader. We conclude that the fuzzy neural network models and their derivations are efficient in constructing a system with a high degree of accuracy and an appropriate level of interpretability working in a wide range of areas of economics and science. •An extensive review of the major aspects of fuzzy neural networks and neuro-fuzzy networks.•Approaches to related work in the literature and history of hybrid models.•Presentation of features and techniques involved in the construction of hybrid models.•Presentation of practical approaches of hybrid models in several applied contexts.
ArticleNumber 106275
Author de Campos Souza, Paulo Vitor
Author_xml – sequence: 1
  givenname: Paulo Vitor
  orcidid: 0000-0002-7343-5844
  surname: de Campos Souza
  fullname: de Campos Souza, Paulo Vitor
  email: paulo.de_campos_souza@jku.at
  organization: Department of Knowledge Based Mathematical Systems, Johannes Kepler University, Linz, Austria
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Snippet This paper presents a review of the central theories involved in hybrid models based on fuzzy systems and artificial neural networks, mainly focused on...
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StartPage 106275
SubjectTerms Fuzzy neural network
Hybrid models
Neuro-fuzzy network
Title Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature
URI https://dx.doi.org/10.1016/j.asoc.2020.106275
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