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Constructing prediction intervals for neural network metamodels of complex systems

Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2009, Constructing prediction intervals for neural network metamodels of complex systems, in IJCNN 2009 : International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1576-1582.

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Title Constructing prediction intervals for neural network metamodels of complex systems
Author(s) Khosravi, Abbas
Nahavandi, Saeid
Creighton, Doug
Conference name IEEE International Joint Conference on Neural Networks (2009 : Atlanta, Georgia)
Conference location Atlanta, Georgia
Conference dates 14-19 Jun. 2009
Title of proceedings IJCNN 2009 : International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2009
Conference series International Joint Conference on Neural Networks
Start page 1576
End page 1582
Publisher IEEE
Place of publication Piscataway, N.J.
Summary A rich literature discussing techniques for adopting neural networks for metamodelling of complex systems exists. The main focus in many studies conducted so far has been on training and utilising neural networks as point estimators/predictors. Uncertainties prevailing within complex systems and dependencies amongst constituent entities are real threats for prediction performance of these types of metamodels. From a practical point of view, an indication of prediction accuracy is necessary before making a decision based on results yielded by a metamodel. In this paper we adopt neural network metamodels for constructing prediction intervals of stochastic system performance measures. Upper and lower bounds of a prediction interval are computed such that the real system performance will lie between them with a high probability. Demonstrated results for a real world case study show that the constructed prediction intervals cover the targets, are more informative and more suited for decision making, when compared with point predictions.
Notes ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 9781424435531
ISSN 1098-7576
Language eng
Field of Research 080602 Computer-Human Interaction
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30029277

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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Created: Sun, 13 Jun 2010, 14:14:23 EST by Leanne Swaneveld