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Author
dc.contributor.author
Zhang Guodaohu_HU
Author
dc.contributor.author
Band Shahab S.hu_HU
Author
dc.contributor.author
Ardabili Sinahu_HU
Author
dc.contributor.author
Chau Kwok-Winghu_HU
Author
dc.contributor.author
Mosavi Amirhu_HU
Availability Date
dc.date.accessioned
2022-04-11T10:31:18Z
Availability Date
dc.date.available
2022-04-11T10:31:18Z
Release
dc.date.issued
2022hu_HU
Issn
dc.identifier.issn
1994-2060hu_HU
uri
dc.identifier.uri
http://hdl.handle.net/20.500.12944/17578
Abstract
dc.description.abstract
The machine learning method of Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as a data-driven technique to model the dew point temperature (DPT). The input patterns, of T min, T max, and T mean, are utilized for the training. The results indicate thatANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the proposed machine learning method and the observed data. Using this prediction toolkit, DPT can be adequately predicted based on the temperature distribution. The modeling approach has shown to be promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales where the ANFIS model remains stable for almost all the numbers of the membership functions.hu_HU
Language
dc.language
enhu_HU
Rent
dc.publisher
Taylor and Francis Ltd.hu_HU
Keywords
dc.subject
ANFIShu_HU
Keywords
dc.subject
artificial intelligencehu_HU
Keywords
dc.subject
bilayer neural networkhu_HU
Keywords
dc.subject
Dew pointhu_HU
Keywords
dc.subject
machine learninghu_HU
Title
dc.title
Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperaturehu_HU
Type
dc.type
folyóiratcikkhu_HU
Version
dc.description.version
publishedhu_HU

dc.rights.accessRights
nyílt hozzáférésűhu_HU
Doi ID
dc.identifier.doi
10.1080/19942060.2022.2043187hu_HU
Discipline Discipline +
dc.subject.discipline
Műszaki tudományokhu_HU

dc.subject.sciencebranch
Műszaki tudományok/Informatikai tudományokhu_HU
MTMT ID
dc.identifier.mtmt
32713794hu_HU

dc.identifier.journalTitle
Engineering Applications of Computational Fluid Mechanicshu_HU

dc.identifier.journalVolume
16hu_HU

dc.identifier.journalIssueNumber
1hu_HU
Scope
dc.format.page
713-723hu_HU
Wos ID
dc.identifier.wos
000762660400001hu_HU
ID Scopus
dc.identifier.scopus
2-s2.0-85125952053hu_HU
Author institution
dc.contributor.department
Információs Társadalom Kutatóintézethu_HU

dc.contributor.faculty
Eötvös József Kutatóközponthu_HU


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Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature
 
 

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