MEASUREMENT UNCERTAINTY CONTRIBUTION TO THE CALIBRATION CURVE FITTING OF AN AERODYNAMIC EXTERNAL BALANCE USING MLP ARTIFICIAL NEURAL NETWORK

I. M. Barbosa, E. del Moral Hernandez, M. L. C. C. Reis, O. A. F. Mello

Abstract

The aim of this study is to fit a calibration curve to a multivariate system. The experimental data are generated from the calibration of the aerodynamic external balance of the subsonic wind n.º 2, the TA-2, of the Brazilian Aerospace Institute, IAE. Multilayer Perceptrons (MLPs) Artificial Neural Networks are employed. To fit the calibration curve, the MLPs are submitted to the learning process. The measurement uncertainties are taken into consideration, through the modification of the MLP learning algorithm, which in its classical approach, considers the data points free from error sources. The results of both methodologies, learning algorithm endowed or without uncertainties, are compared.

Keywords

multilayer perceptrons, calibration curve, measurement uncertainty, wind tunnel, repeatability

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PWC-2006-TC7-008u.pdf

DOI

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IMEKO TC

TC7 - Measurement Science

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