Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
Expert Systems with Applications · Ekim 2011
YÖKSİS Kayıtları
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
Expert Systems with Applications · 2011 SCI-Expanded 24 atıf
Prof. Dr. İSMAİL SARITAŞ →
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
Expert Systems with Applications · 2011 SCI-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
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Makale Bilgileri
ISSN09574174
Yayın TarihiEkim 2011
Cilt / Sayfa38 · 13912-13923
Scopus ID2-s2.0-79959967408
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.
Yazarlar (4)
1
Şakir Taşdemir
2
Ismail Saritas
3
Murat Ciniviz
4
Novruz Allahverdi
Anahtar Kelimeler
Artificial neural network
Engine emission
Engine performance
Fuzzy expert system
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Metrikler
76
Atıf
4
Yazar
4
Anahtar Kelime