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research
Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement
Authors
Alvanitopoulos
Ayoubi
+35 more
Berenji
Chatterjee
Chen
Eristi
Evsukoff
Frey
Jang
Jha
Juang
K.B. Kis
Kar
Kasabov
Kasabov
Khoshnevisan
Kohonen
Lee
Lei
Lin
Mahapatra
Mamdani
McCulloch
Moharana
Nuck
Pouran
Roshani
Taghavifar
Takagi
Wali
Wang
Ye
Zadeh
Zhang
Zio
Zs.J. Viharos
Übeyli
Publication date
1 January 2015
Publisher
'Elsevier BV'
Doi
Cite
Abstract
Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input-output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. © 2015 Elsevier Ltd
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oai:eprints.sztaki.hu:8231
Last time updated on 09/11/2016
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info:doi/10.1016%2Fj.measureme...
Last time updated on 01/04/2019