Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59153-7_24 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT I |
Keywords | Field | DocType |
Sine-fitting methods,Spectral analysis,ADALINE,ANN,Digital measurement,Uncertainty,Monte-Carlo,DFT | Monte Carlo method in statistical physics,Monte Carlo method,Pattern recognition,Computer science,Hybrid Monte Carlo,Quasi-Monte Carlo method,Algorithm,Uncertainty analysis,Artificial intelligence,Dynamic Monte Carlo method,Discrete Fourier transform,Monte Carlo molecular modeling | Conference |
Volume | ISSN | Citations |
10305 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. R. Salinas | 1 | 0 | 1.01 |
| F. García | 2 | 27 | 7.39 |
| Javier Diaz de Aguilar | 3 | 0 | 1.01 |
| Gonzalo Joya | 4 | 0 | 1.01 |
| Francisco Sandoval | 5 | 237 | 22.18 |