@ARTICLE{Coral_Rodrigo_A_2016,
 author={Coral, Rodrigo and Flesch, Carlos A. and Penz, Cesar A. and Roisenberg, Mauro and Pacheco, Antonio L.S.},
 volume={vol. 23},
 number={No 2},
 journal={Metrology and Measurement Systems},
 pages={281-294},
 howpublished={online},
 year={2016},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={When an artificial neural network is used to determine the value of a physical quantity its result is usually presented without an uncertainty. This is due to the difficulty in determining the uncertainties related to the neural model. However, the result of a measurement can be considered valid only with its respective measurement uncertainty. Therefore, this article proposes a method of obtaining reliable results by measuring systems that use artificial neural networks. For this, it considers the Monte Carlo Method (MCM) for propagation of uncertainty distributions during the training and use of the artificial neural networks.},
 type={Artykuły / Articles},
 title={A Monte Carlo-Based Method for Assessing the Measurement Uncertainty in the Training and Use of Artificial Neural Networks},
 URL={http://www.czasopisma.pan.pl/Content/90414/PDF/10.1515-mms-2016-0015%20paper11.pdf},
 doi={10.1515/mms-2016-0015},
 keywords={artificial neural networks, measurement system, measurement uncertainty, Monte Carlo method},
}