TY - JOUR N2 - 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. L1 - http://journals.pan.pl/Content/90414/PDF/10.1515-mms-2016-0015%20paper11.pdf L2 - http://journals.pan.pl/Content/90414 PY - 2016 IS - No 2 EP - 294 DO - 10.1515/mms-2016-0015 KW - artificial neural networks KW - measurement system KW - measurement uncertainty KW - Monte Carlo method A1 - Coral, Rodrigo A1 - Flesch, Carlos A. A1 - Penz, Cesar A. A1 - Roisenberg, Mauro A1 - Pacheco, Antonio L.S. PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation VL - vol. 23 DA - 2016.06.30 T1 - A Monte Carlo-Based Method for Assessing the Measurement Uncertainty in the Training and Use of Artificial Neural Networks SP - 281 UR - http://journals.pan.pl/dlibra/publication/edition/90414 T2 - Metrology and Measurement Systems ER -