E2.3 Propagation of uncertainty for an Adaptive Linear Approximation algorithm
- SMSI 2020
(did not take place because of Covid-19 virus pandemic)
- SMSI 2020 - System of Units and Metrological Infrastructure
- E2 Future Topics in Metrology
- T. Dorst, S. Eichstädt - Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin (Germany), T. Schneider - ZeMA – Center for Mechatronics and Automation Technology gGmbH, Saarbrücken, (Germany), A. Schütze - Saarland University, Saarbrücken (Germany)
- 366 - 367
In machine learning, a variety of algorithms are available for feature extraction. To obtain reliable features from measured data, the propagation of measurement uncertainty is proposed here in line with the Guide to the Expression of Uncertainty in Measurement (GUM). Recently, methods for the discrete Fourier and Wavelet transform have been published. Here, the Adaptive Linear Approximation (ALA) as a further complementary feature extraction algorithm is considered in combination with an analytical model for the uncertainty evaluation of the ALA features.