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

Rodrigo Coral, Carlos A. Flesch, Cesar A. Penz, Mauro Roisenberg, Antonio L. S. Pacheco
2016 Metrology and Measurement Systems  
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
more » ... this, it considers the Monte Carlo Method (MCM) for propagation of uncertainty distributions during the training and use of the artificial neural networks.
doi:10.1515/mms-2016-0015 fatcat:k6b4crvkwregxixsxjw6cy3cpu