MEASUREMENT UNCERTAINTY CONTRIBUTION TO THE MLP NEURAL NETWORK LEARNING ALGORITHM APPLIED TO AERODYNAMIC EXTERNAL BALANCE CALIBRATION CURVE FITTING
Journal Title: Metrology and Measurement Systems - Year 2007, Vol 14, Issue 1
Abstract
The aim of this study is to fit a calibration curve to a multivariate measurement system considering uncertainty in load measurements. The experimental data are generated from the calibration of the aerodynamic external balance of the subsonic wind tunnel n.o 2, at the Brazilian Institute of Aeronautics and Space. To fit the calibration curve, Multilayer Perceptron Artificial Neural Networks are submitted to the learning process. Studies employing several different architectures were carried out in order to improve the MLP convergence. The uncertainty in measurements is taken into consideration, through the modification of the learning algorithm, which in its classical approach, considers the data points free from error sources. The results of both methodologies, learning algorithm endowed with and without uncertainties, are compared. The artificial neural network performance in predicting future calibration data is explored through the simulation process.
Authors and Affiliations
I. BARBOSA, E. HERNANDEZ, M. REIS, O. MELLO
Keywords
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- EP ID EP160107
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