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Developing optimal neural network metamodels based on prediction intervals

Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2009, Developing optimal neural network metamodels based on prediction intervals, in IJCNN 2009 : International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1583-1589.

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Title Developing optimal neural network metamodels based on prediction intervals
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 1583
End page 1589
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Finding optimal structures for neural networks is remains an open problem, despite the rich array of literature on the application of neural networks in different areas of science and engineering. The stochastic nature of operations common in complex systems makes point prediction performance of neural network metamodels an additional challenge. We propose a method for selecting the best structure of a neural network metamodel. For selecting the network structure, the new method uses interval prediction capability of neural networks and chooses a topology that yields the narrowest prediction band for targets. This is an improvement on traditional criteria, such as mean square error or mean absolute percentage error.

As a case study, the interval prediction method is applied to a metamodel of a complex system composed of many inextricably interconnected entities and stochastic processes. The demonstrated results expressly show that selecting the network structure based on the proposed method yields more reliable estimates.
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:30029278

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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Created: Sun, 13 Jun 2010, 14:27:51 EST by Leanne Swaneveld