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Lookup NU author(s): Dr Jie ZhangORCiD
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Currently, combining multiple neural networks appears to be a very promising approach in improving neural network generalisation since it is very difficult, if not impossible, to develop a perfect single neural network. In this paper, individual networks are developed from bootstrap re-samples of the original training and testing data sets. Instead of combining all the developed networks, this paper proposes selective combination techniques: forward selection. These techniques essentially combine those individual networks that, when combined, can significantly improve model generalisation. The proposed techniques are applied to modelling irreversible exothermic reaction in CSTR. Application results demonstrate that the proposed techniques can significantly improve model generalisation and perform better than aggregating all the individual networks. © 2006 IEEE.
Author(s): Ahmad Z, Zhang J, Syukor S
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Conference on Computing and Informatics (ICOCI '06)
Year of Conference: 2006
Publisher: IEEE
URL: http://dx.doi.org/10.1109/ICOCI.2006.5276547
DOI: 10.1109/ICOCI.2006.5276547
Library holdings: Search Newcastle University Library for this item
ISBN: 9781424402199
