Measurement uncertainty contribution to the MLP neural network learning algorithm applied to an aerodynamic external balance calibration curve fitting

• Autorzy: Barbosa I. M. , del Moral Hernandez E. , Reis M. L. C. C. , Mello O. A. F.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Multilayer Perceptrons Artificial Neural Network; calibration curve fitting; uncertainty in measurements; external aerodynamic balance; wind tunnel tests

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2007
• Tom: Vol. 14, nr 1
• Strony: 101--115
• Opis fizyczny: Bibliogr. 9 poz., rys., wykr., tab.

Twórcy

autor

Barbosa I. M.

autor

del Moral Hernandez E.

autor

Reis M. L. C. C.

autor

Mello O. A. F.

• Universidade de Sao Paulo, Brasil,

Bibliografia

• 1. ISO/IEC 17025: General Requirements for the Competence of Testing and Calibration Laboratories, 2005.
• 2. ISO: Guide to the Expression of Uncertainty in Measurement, 1995.
• 3. ISO: International Vocabulary of Basic and General Terms in Metrology, 1993.
• 4. Reis M. L. C. C., Vieira W. J., Barbosa I. M., Mello O. A. F., Santos L. A.: Validation of an External Six-Component Wind Tunnel Balance Calibration. 24th AIAA Aerodynamic Measurement Technology and Ground Testing Conference, AIAA, Portland, 2004.
• 5. Reis M. L. C. C., Mello O. A. F., Sadahaki U.: Calibration Uncertainty of an External Six-Component Wind Tunnel Balance. 33rd AIAA Fluid Dynamics Conference and Exhibit, AIAA, Florida, 2003.
• 6. Barbosa I. M.: Application of the Artificial Neural Networks in Metrological Reliability (Aplicação das Redes Neurais Artificiais na Confiabilidade Metrológica). Master of Science dissertation, São Paulo, Brasil, 2004.
• 7. Haykin S. S.: Neural Network: A Comprehensive Foundation. 2nd ed., Prentice-Hall, 1999.
• 8. Lira I.: Evaluating the Measurement Uncertainty – Fundamental and Practical Guidance. Institute of Physics Publishing, IOP, 2002.
• 9. Weise K.: Treatment of Uncertainties in Precision Measurements Uncertainty. IEEE Transactions Instrumentation Measurements, vol. IM-36, no. 2, pp. 642-645, 1987.