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 -