The comparison of performance by using alternative refrigerant R152a in automobile climate system with different artificial neural network models

International Physics Conference at the Anatolian Peak 2016

Kalkisim, A.T.^1 ; Hasiloglu, A.S.^2 ; Bilen, K.^3

MYO Mechatronic, Gumushane University, Baglarbasi, Gumushane

29100, Turkey^1

Computer Engineering, Ataturk University, Erzurum

25100, Turkey^2

Mechanical Engineering, Ataturk University, Erzurum

25100, Turkey^3

关键词: Accurate prediction;    Alternative refrigerant;    Artificial neural network models;    Automobile air conditioning;    Comparison of performance;    Conjugate gradient descents;    Global warming impact;    Neural network model;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/707/1/012047/pdf
DOI  :  10.1088/1742-6596/707/1/012047

来源: IOP

【 摘 要 】

Due to the refrigerant gas R134a which is used in automobile air conditioning systems and has greater global warming impact will be phased out gradually, an alternative gas is being desired to be used without much change on existing air conditioning systems. It is aimed to obtain the easier solution for intermediate values on the performance by creating a Neural Network Model in case of using a fluid (R152a) in automobile air conditioning systems that has the thermodynamic properties close to each other and near-zero global warming impact. In this instance, a network structure giving the most accurate result has been established by identifying which model provides the best education with which network structure and makes the most accurate predictions in the light of the data obtained after five different ANN models was trained with three different network structures. During training of Artificial Neural Network, Quick Propagation, Quasi-Newton, Levenberg-Marquardt and Conjugate Gradient Descent Batch Back Propagation methodsincluding five inputs and one output were trained with various network structures. Over 1500 iterations have been evaluated and the most appropriate model was identified by determining minimum error rates. The accuracy of the determined ANN model was revealed by comparing with estimates made by the Multi-Regression method.