Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) |
| Untertitel | 2013 IEEE Symposium Series on Computational Intelligence (SSCI) |
| Seiten | 118-124 |
| Seitenumfang | 7 |
| Publikationsstatus | Veröffentlicht - 2013 |
| Extern publiziert | Ja |
| Veranstaltung | 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapur Dauer: 16 Apr. 2013 → 19 Apr. 2013 |
Abstract
An artificial 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 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-forward ANN 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 Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Angewandte Informatik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES): 2013 IEEE Symposium Series on Computational Intelligence (SSCI). 2013. S. 118-124 6611738.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - An artificial neural network based approach for power spectrum estimation and simulation of stochastic processes subject to missing data
AU - Comerford, Liam A.
AU - Kougioumtzoglou, Ioannis A.
AU - Beer, Michael
PY - 2013
Y1 - 2013
N2 - An artificial 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 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-forward ANN 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 - An artificial 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 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-forward ANN 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).
KW - environmental loads
KW - missing data
KW - Monte Carlo simulation
KW - neural networks
KW - stochastic processes
UR - http://www.scopus.com/inward/record.url?scp=84886531265&partnerID=8YFLogxK
U2 - 10.1109/CIES.2013.6611738
DO - 10.1109/CIES.2013.6611738
M3 - Conference contribution
AN - SCOPUS:84886531265
SN - 9781467358514
SP - 118
EP - 124
BT - Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)
T2 - 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -