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2017 | OriginalPaper | Buchkapitel

Uncertainty Analysis of ANN Based Spectral Analysis Using Monte Carlo Method

verfasst von : José Ramón Salinas, Francisco García-Lagos, Javier Díaz de Aguilar, Gonzalo Joya, Francisco Sandoval

Erschienen in: Advances in Computational Intelligence

Verlag: Springer International Publishing

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Abstract

Uncertainty analysis of an Artificial Neural Network (ANN) based method for spectral analysis of asynchronously sampled signals is performed. Main uncertainty components contributions, jitter and quantization noise, are considered in order to obtain the signal amplitude and phase uncertainties using Monte Carlo method. The analysis performed identifies also uncertainties main contributions depending on parameters configurations. The analysis is performed simultaneously with the proposed method and two others: Discrete Fourier Transform (DFT) and Multiharmonic Sine Fitting Method (MSFM), in order to compare them in terms of uncertainty. Results show the proposed method has the same uncertainty as DFT for amplitude values and around double uncertainty in phase values.

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Titel: Uncertainty Analysis of ANN Based Spectral Analysis Using Monte Carlo Method
verfasst von: José Ramón Salinas
Francisco García-Lagos
Javier Díaz de Aguilar
Gonzalo Joya
Francisco Sandoval
Verlag: Springer International Publishing
Buch: Advances in Computational Intelligence
Print ISBN: 978-3-319-59152-0

Electronic ISBN: 978-3-319-59153-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59153-7_24

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