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.
(Some files may be inaccessible until you login with your Deakin Research Online credentials)
IJCNN 2009 : International Joint Conference on Neural Networks
International Joint Conference on Neural Networks
Place of publication
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.
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact firstname.lastname@example.org.