Scopus Eşleşmesi Bulundu
71
Atıf
38
Cilt
13912-13923
Sayfa
Scopus Yazarları: Şakir Taşdemir, Ismail Saritas, Murat Ciniviz, Novruz Allahverdi
Özet
This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work. © 2011 Elsevier Ltd. All rights reserved.
Anahtar Kelimeler (Scopus)
Artificial neural network
Engine emission
Engine performance
Fuzzy expert system
Scimago Dergi Bilgisi
Otomatik ISSN Eşleştirmesi
2011 yılı verileri
Expert Systems with Applications
Q1
SJR Quartile
1,113
SJR Skoru
290
H-Index
Kategoriler: Artificial Intelligence (Q1) · Computer Science Applications (Q1) · Engineering (miscellaneous) (Q1)
Alanlar: Computer Science · Engineering
Ülke: United Kingdom
· Elsevier Ltd
Bu bilgiler makale yılına göre Scimago veritabanından ISSN eşleştirmesiyle otomatik getirilmektedir.
Dergi sıralama verileri Scimago'nun ilgili yılı baz alınmaktadır.
Anahtar Kelimeler
Artificial neural network
Engine emission
Engine performance
Fuzzy expert system
Makale Bilgileri
Dergi
Expert Systems with Applications
ISSN
0957-4174
Yıl
2011
/ 5. ay
Cilt / Sayı
/ 11
Sayfalar
13912 – 13923
Makale Türü
Özgün Makale
Hakemlik
Hakemli
Endeks
SCI-Expanded
JCR Quartile
Q1
YÖKSİS Atıf
24
Yayın Dili
İngilizce
Kapsam
Uluslararası
Toplam Yazar
4 kişi
Erişim Türü
Basılı+Elektronik
Erişim Linki
Makaleye Git
Alan
Mühendislik Temel Alanı
Elektrik-Elektronik ve Haberleşme Mühendisliği
Devreler ve Sistemler Teorisi
Yapay Zeka
YÖKSİS Yazar Kaydı
Yazar Adı
TAŞDEMİR ŞAKİR,SARITAŞ İSMAİL,CİNİVİZ MURAT,ALLAHVERDİ NOVRUZ
YÖKSİS ID
799656
Hızlı Erişim
Metrikler
YÖKSİS Atıf
24
Scopus Atıf
71
JCR Quartile
Q1
Yazar Sayısı
4