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A comparison of different methods for combining multiple neural networks models

Lookup NU author(s): Zainal Ahmad, Dr Jie ZhangORCiD


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A single neural network model developed from a limited amount of data usually lacks robustness. Neural network model robustness can be enhanced by combining multiple neural networks. There are several approaches for combining neural networks. A comparison of these methods on three non-linear dynamic system modelling case studies is carried out in this paper. It is shown that selective combination and combining networks of various structures generally improve model performance. The principal component regression approaches generally give quite consistent good performance.

Publication metadata

Author(s): Zhang J; Ahmad Z

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Joint Conference on Neural Networks (IJCNN 02)

Year of Conference: 2002

Pages: 828-833

Publisher: IEEE


DOI: 10.1109/IJCNN.2002.1005581

Library holdings: Search Newcastle University Library for this item

ISBN: 0780372786