Features of Metaheuristic Algorithm for Integration with ANFIS Model

In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and optimization algorithms have been more common for solving regression and classification problems. In the Adaptive Neuro-fuzzy inference system(ANFIS), many researchers used the adaption of metaheuristic algorithms with ANFIS to propose the best estimation model. However, many researchers only focused on the experiment without the demonstration mathematical or indicating which characteristic of optimization algorithm, during the run, affect and settable in coordination with ANFIS. The paper provides an adaption of metaheuristic algorithms with ANFIS which has been performed by considering accuracy parameters in layer 1 and layer 4 for the estimation problem. It is integrated six well-known metaheuristic algorithms and extracting the characteristic of them. In the experiment, the metaheuristic algorithms based on the evolutionary computation have been demonstrated more stable than swarm intelligence methods in tuning parameters of ANFIS. © 2022 IEEE.

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Eser Adı
(dc.title)
Features of Metaheuristic Algorithm for Integration with ANFIS Model
Yazar
(dc.contributor.author)
Aref Yelghı
Yayın Yılı
(dc.date.issued)
2022
Tür
(dc.type)
Makale
Özet
(dc.description.abstract)
In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and optimization algorithms have been more common for solving regression and classification problems. In the Adaptive Neuro-fuzzy inference system(ANFIS), many researchers used the adaption of metaheuristic algorithms with ANFIS to propose the best estimation model. However, many researchers only focused on the experiment without the demonstration mathematical or indicating which characteristic of optimization algorithm, during the run, affect and settable in coordination with ANFIS. The paper provides an adaption of metaheuristic algorithms with ANFIS which has been performed by considering accuracy parameters in layer 1 and layer 4 for the estimation problem. It is integrated six well-known metaheuristic algorithms and extracting the characteristic of them. In the experiment, the metaheuristic algorithms based on the evolutionary computation have been demonstrated more stable than swarm intelligence methods in tuning parameters of ANFIS. © 2022 IEEE.
Açık Erişim Tarihi
(dc.date.available)
2022-09-29
Yayıncı
(dc.publisher)
Conference Proceedings
Dil
(dc.language.iso)
En
Konu Başlıkları
(dc.subject)
ANFIS
Konu Başlıkları
(dc.subject)
Crossover
Konu Başlıkları
(dc.subject)
Genetic Algorithm
Konu Başlıkları
(dc.subject)
Metaheuristics Algorithm
Konu Başlıkları
(dc.subject)
Mutation
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.14081/1787
Dergi
(dc.relation.journal)
2022 Internaiıonal Conference On Theoreiıcal And Applıed Computer Science And Engineering (Ictasce)
Esere Katkı Sağlayan
(dc.contributor.other)
Yelghi, Aref
Esere Katkı Sağlayan
(dc.contributor.other)
Tavangari, Shirmohammad
DOI
(dc.identifier.doi)
10.1109/ICTACSE50438.2022.10009722
Bitiş Sayfası
(dc.identifier.endpage)
31
Başlangıç Sayfası
(dc.identifier.startpage)
29
wosauthorid
(dc.contributor.wosauthorid)
CHZ-0386-2022
Department
(dc.contributor.department)
Bilgisayar Mühendisliği
Wos No
(dc.identifier.wos)
WOS:000932842500003
Veritabanları
(dc.source.platform)
Wos
Veritabanları
(dc.source.platform)
Scopus
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