Details

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

Numer

No 1

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Date

2015[2015.01.01 AD - 2015.12.31 AD]

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

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