Details
| Original language | English |
|---|---|
| Title of host publication | Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures |
| Subtitle of host publication | Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 |
| Place of Publication | Boca Raton |
| Pages | 1083-1090 |
| Number of pages | 8 |
| ISBN (electronic) | 978-1-315-88488-2 |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 - New York, NY, United States Duration: 16 Jun 2013 → 20 Jun 2013 |
Abstract
AnArtificial Neural Network (ANN) approach is presented as a possible solution to overcoming the problems associated with missing data in spectral analysis and/or simulation of stationary and non-stationary stochastic processes. By using an ANN to capture patterns present in the available data, gaps can then be filled or entirely new processes generated.A feed-forwardANN is used with ordered inputs and Gaussian white noise to represent missing data during learning. The solution is broadly applicable in many circumstances due to the fact that it assumes no prior knowledge of the underlying statistics of the process. Specifically, to present the method in context, this paper addresses some of the challenges associated with preparing data for environmental simulation load models (time dependent, 1-dimensional).
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
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Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures: Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013. Boca Raton, 2013. p. 1083-1090.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - An artificial neural network based approach for power spectrum estimation subject to limited and/or missing data
AU - Comerford, L. A.
AU - Kougioumtzoglou, I. A.
AU - Beer, M.
PY - 2013
Y1 - 2013
N2 - AnArtificial Neural Network (ANN) approach is presented as a possible solution to overcoming the problems associated with missing data in spectral analysis and/or simulation of stationary and non-stationary stochastic processes. By using an ANN to capture patterns present in the available data, gaps can then be filled or entirely new processes generated.A feed-forwardANN is used with ordered inputs and Gaussian white noise to represent missing data during learning. The solution is broadly applicable in many circumstances due to the fact that it assumes no prior knowledge of the underlying statistics of the process. Specifically, to present the method in context, this paper addresses some of the challenges associated with preparing data for environmental simulation load models (time dependent, 1-dimensional).
AB - AnArtificial Neural Network (ANN) approach is presented as a possible solution to overcoming the problems associated with missing data in spectral analysis and/or simulation of stationary and non-stationary stochastic processes. By using an ANN to capture patterns present in the available data, gaps can then be filled or entirely new processes generated.A feed-forwardANN is used with ordered inputs and Gaussian white noise to represent missing data during learning. The solution is broadly applicable in many circumstances due to the fact that it assumes no prior knowledge of the underlying statistics of the process. Specifically, to present the method in context, this paper addresses some of the challenges associated with preparing data for environmental simulation load models (time dependent, 1-dimensional).
UR - http://www.scopus.com/inward/record.url?scp=84892387339&partnerID=8YFLogxK
UR - https://doi.org/10.1201/b16387
M3 - Conference contribution
AN - SCOPUS:84892387339
SN - 9781138000865
SP - 1083
EP - 1090
BT - Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures
CY - Boca Raton
T2 - 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013
Y2 - 16 June 2013 through 20 June 2013
ER -