Artificial neural network based modelling of performance of a beta-type Stirling engine

2012 ◽  
Vol 227 (3) ◽  
pp. 166-177 ◽  
Author(s):  
Yaşar Önder Özgören ◽  
Selim Çetinkaya ◽  
Suat Sarıdemir ◽  
Adem Çiçek ◽  
Fuat Kara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stirling Engine ◽  
Artificial Neural

Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network

2013 ◽  
Vol 67 ◽  
pp. 357-368 ◽  
Author(s):  
Yaşar Önder Özgören ◽  
Selim Çetinkaya ◽  
Suat Sarıdemir ◽  
Adem Çiçek ◽  
Fuat Kara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Modeling ◽  
Stirling Engine ◽  
Artificial Neural

Artificial neural network, ANN-PSO and ANN-ICA for modelling the Stirling engine

2015 ◽  
Vol 37 (5) ◽  
pp. 456-468 ◽  
Author(s):  
Somayeh Toghyani ◽  
Mohammad H. Ahmadi ◽  
Alibakhsh Kasaeian ◽  
Amir H. Mohammadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stirling Engine ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Intelligent speed control of a Stirling engine using artificial neural network

2021 ◽  
Author(s):  
Mohammadreza Hojati ◽  
Ehsan Mansouri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Engine Speed ◽  
Combustion Engine ◽  
Mechanical Energy ◽  
Stirling Engine ◽  
Ambient Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
External Combustion

Abstract The Stirling engine is a kind of external combustion engine that is ideal for converting renewable energies to electricity or mechanical energy forms. Therefore, monitoring and controlling some of its parameters such as its speed is important. The purpose of this paper is to control a Stirling engine flywheel rotation speed using an intelligent parameter predictor. Since various parameters affect motor performance, determining the optimal value of them manually is impossible. Therefore, linking these parameters together and finding a relationship between them would be beneficial. Hence, using artificial intelligence (AI) to find a quick and efficient solution is particularly important. At the studied Stirling engine, speed, cold sink and ambient temperatures are defined as input parameters, and hot sink temperature is considered as the output parameter that should be calculated. To discover the relationship between inputs and output, an artificial neural network (ANN) is used. The results of this study showed that the use of ANNs can be significantly helpful in controlling engine speed.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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