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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Notes
- 1.
Although there is a coincidence between some terms used in GUM and this work nomenclatures, since there is no possibility of confusion, GUM nomenclature has been maintained in this section in order to simplify its reading.
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Acknowledgment
This work was partially supported by the Universidad de Malaga - Campus de Excelencia Andalucia-Tech.
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Salinas, J.R., García-Lagos, F., de Aguilar, J.D., Joya, G., Sandoval, F. (2017). Uncertainty Analysis of ANN Based Spectral Analysis Using Monte Carlo Method. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_24
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DOI: https://doi.org/10.1007/978-3-319-59153-7_24
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