Details
| Original language | English |
|---|---|
| Pages (from-to) | 150-160 |
| Number of pages | 11 |
| Journal | Structural Safety |
| Volume | 52 |
| Issue number | Part B |
| Early online date | 18 Nov 2014 |
| Publication status | Published - Jan 2015 |
| Externally published | Yes |
Abstract
An artificial neural network (ANN) based approach is developed for estimating the power spectrum of stochastic processes subject to missing/limited data. In this regard, an appropriately defined ANN is utilized to capture the stochastic pattern in the available data in an average sense. Next, the extrapolation capabilities of the ANN are exploited for generating realizations of the underlying stochastic process. Finally, power spectrum estimates are derived based on established frequency (e.g. Fourier analysis), or versatile joint time-frequency analysis techniques (e.g. wavelets) for the cases of stationary and non-stationary stochastic processes, respectively. One of the significant advantages of the approach relates to the fact that no a priori knowledge about the data is assumed, while the approach is applicable for treating non-stationary processes not only with separable but non-separable in time and frequency evolutionary power spectra as well. Comparisons of several target power spectra with Monte Carlo simulation based power spectrum estimates demonstrate the versatility and reliability of the approach for up to 50% missing data.
Keywords
- Evolutionary power spectrum, Missing data, Neural network, Stochastic process
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Engineering(all)
- Safety, Risk, Reliability and Quality
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In: Structural Safety, Vol. 52, No. Part B, 01.2015, p. 150-160.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - An artificial neural network approach for stochastic process power spectrum estimation subject to missing data
AU - Comerford, Liam
AU - Kougioumtzoglou, Ioannis A.
AU - Beer, Michael
PY - 2015/1
Y1 - 2015/1
N2 - An artificial neural network (ANN) based approach is developed for estimating the power spectrum of stochastic processes subject to missing/limited data. In this regard, an appropriately defined ANN is utilized to capture the stochastic pattern in the available data in an average sense. Next, the extrapolation capabilities of the ANN are exploited for generating realizations of the underlying stochastic process. Finally, power spectrum estimates are derived based on established frequency (e.g. Fourier analysis), or versatile joint time-frequency analysis techniques (e.g. wavelets) for the cases of stationary and non-stationary stochastic processes, respectively. One of the significant advantages of the approach relates to the fact that no a priori knowledge about the data is assumed, while the approach is applicable for treating non-stationary processes not only with separable but non-separable in time and frequency evolutionary power spectra as well. Comparisons of several target power spectra with Monte Carlo simulation based power spectrum estimates demonstrate the versatility and reliability of the approach for up to 50% missing data.
AB - An artificial neural network (ANN) based approach is developed for estimating the power spectrum of stochastic processes subject to missing/limited data. In this regard, an appropriately defined ANN is utilized to capture the stochastic pattern in the available data in an average sense. Next, the extrapolation capabilities of the ANN are exploited for generating realizations of the underlying stochastic process. Finally, power spectrum estimates are derived based on established frequency (e.g. Fourier analysis), or versatile joint time-frequency analysis techniques (e.g. wavelets) for the cases of stationary and non-stationary stochastic processes, respectively. One of the significant advantages of the approach relates to the fact that no a priori knowledge about the data is assumed, while the approach is applicable for treating non-stationary processes not only with separable but non-separable in time and frequency evolutionary power spectra as well. Comparisons of several target power spectra with Monte Carlo simulation based power spectrum estimates demonstrate the versatility and reliability of the approach for up to 50% missing data.
KW - Evolutionary power spectrum
KW - Missing data
KW - Neural network
KW - Stochastic process
UR - http://www.scopus.com/inward/record.url?scp=84918547613&partnerID=8YFLogxK
U2 - 10.1016/j.strusafe.2014.10.001
DO - 10.1016/j.strusafe.2014.10.001
M3 - Article
AN - SCOPUS:84918547613
VL - 52
SP - 150
EP - 160
JO - Structural Safety
JF - Structural Safety
SN - 0167-4730
IS - Part B
ER -