Title

Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times

Journal title

Metrology and Measurement Systems

Yearbook

2015

Volume

vol. 22

Numer

No 1

Authors

Coral, Rodrigo ; Flesch, Carlos A. ; Penz, Cesar A. ; Borges, Maikon R.

Keywords

refrigeration compressor ; artificial neural networks ; performance test

Divisions of PAS

Nauki Techniczne

Coverage

79-88

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Date

2015[2015.01.01 AD - 2015.12.31 AD]

Type

Artykuły / Articles

Identifier

ISSN 0860-8229

References

Russel (2003), Artificial A Modern Approach , second ed New York, Intelligence. ; Ertunc (2005), Artificial neural network analysis of a refrigeration system with an evaporative condenser, Appl Therm Eng, 26, 627, doi.org/10.1016/j.applthermaleng.2005.06.002 ; Penz (2012), Fuzzy - Bayesian network for refrigeration compressor performance prediction and test time reduction Expert with, Syst Appl, 39, 4268, doi.org/10.1016/j.eswa.2011.09.107 ; Papadopoulos (2000), Confidence estimation methods for neural networks : a practical comparison In of the Eur on Artif Neural Netw, Proc Symp, 75. ; Zhang (1999), Developing robust non - linear models through bootstrap aggregated neural networks, Neurocomputing, 25, 93, doi.org/10.1016/S0925-2312(99)00054-5 ; Haykin (1999), Neural Networks : a comprehensive foundation India, Education. ; Gayeski (2010), Empirical modeling of a rolling - piston compressor heat pump for predictive control in low lift cooling ASHRAE, Trans, 116. ; Wu (2010), A neural network ensemble model for on - line monitoring of process mean and variance shifts in correlated processes Expert with, Syst Appl, 37, 4058, doi.org/10.1016/j.eswa.2009.11.051 ; ASHRAE (2005), STANDARD ANSI Methods of testing for rating positive displacement refrigerant compressors and condensing units, USA, 23. ; Zio (2006), A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes on Nucl, IEEE Trans Sci, 53, 1460, doi.org/10.1109/TNS.2006.871662 ; Yu (2009), Neural network ensemble - based model for online monitoring and diagnosis of out - of - control signals in multivariate manufacturing processes Expert with, Syst Appl, 36, 909, doi.org/10.1016/j.eswa.2007.10.003 ; Singaram (2011), Prediction models for mechanical properties of AZ MG alloy fabricated by equal channel angular pressing of and in, Int J Res Rev Appl Sci, 8, 337. ; Trichakis (2011), Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response Processes, Hydrol, 25, 2827. ; Ghobadian (2009), Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network, Renew Energy, 34, 976, doi.org/10.1016/j.renene.2008.08.008 ; Arcaklioğlu (2004), Thermodynamic analysis of refrigerant mixtures using artificial neural networks, Appl Energy, 78, 219, doi.org/10.1016/j.apenergy.2003.08.001 ; Kim (1995), Feedforward neural networks for fault diagnosis and severity assessment of a screw compressor and Signal Processing, Mech Syst, 9, 485. ; Swider (2001), Modelling of vapour - compression liquid chillers with neural networks, Appl Therm Eng, 21, 311, doi.org/10.1016/S1359-4311(00)00036-3 ; Gustafson (1992), Correlation of transient and steady - state compressor performance using neural networks In of the AutoTest Conf, Proc USA, 69. ; Flesch (2010), Modelling identification and control of a calorimeter used for performance evaluation of refrigerant compressors Control, Eng Pract, 18, 254, doi.org/10.1016/j.conengprac.2009.11.003 ; Granitto (2005), Neural Networks Ensembles : Evaluation of Aggregation algorithms, Artif Intelligence, 163, 139, doi.org/10.1016/j.artint.2004.09.006

DOI

10.1515/mms-2015-0003