Details

Title

A Monte Carlo-Based Method for Assessing the Measurement Uncertainty in the Training and Use of Artificial Neural Networks

Journal title

Metrology and Measurement Systems

Yearbook

2016

Volume

vol. 23

Numer

No 2

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Date

2016

Identifier

ISSN 0860-8229

References

Bakhary (2007), Damage detection using artificial neural network with consideration of uncertainties Structures, Eng, 29, 2806. ; Chryssolouris (1996), Confidence interval prediction for neural network models on Neural Netw, IEEE Trans, 7, 229. ; Ertunc (2006), 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 ; Zhu (2005), One parameterized model of indirect measurement based on neural network and its sensitivity coefficient computing th Int on Measurement Technol and Intell of Phys : Conf Series, Proc Symp, 7. ; Haykin (1999), Neural A Comprehensive Foundation nd ed, Networks. ; Neto (2013), Comparative study on local and global strategies for confidence estimation in neural networks and extensions to improve their predictive power &, Neural Comput Appl, 22, 1519, doi.org/10.1007/s00521-012-1051-x ; Singaram (2011), ANN prediction models for mechanical properties of AZ MG alloy fabricated by equal channel angular pressing of and Reviews in Appl, Int J Res Sciences, 8, 337. ; Papadopoulos (2001), Confidence estimation methods for neural networks : a practical comparison on Neural Netw, IEEE Trans, 12, 1278. ; Fortuna (2007), Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery on Instrum and Measurement, IEEE Trans, 56, 95. ; Ahmad (2002), A comparison of different methods for combining multiple neural networks models Joint Conf on Neural Netw, Proc Int, 1, 828. ; Arcaklioğlu (2004), Thermodynamic analyses of refrigerant mixtures using artificial neural networks, Appl Energy, 78, 219, doi.org/10.1016/j.apenergy.2003.08.001 ; Edwards (2002), Minimizing risk using prediction uncertainty in neural network estimation fusion and its application to papermaking on Neural Netw, IEEE Trans, 13, 726. ; Russel (2003), Artificial A Modern Approach nd ed, Intelligence. ; De Veaux (1998), Prediction intervals for neural networks via nonlinear regression, Technometrics, 40, 273, doi.org/10.2307/1270528 ; 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 ; Methaprayoon (2007), An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty, IEEE Trans Ind Appl, 43, 1441, doi.org/10.1109/TIA.2007.908203

DOI

10.1515/mms-2016-0015

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