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Improving long range prediction for nonlinear process modelling through combining multiple neural networks

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


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Different methods for combining multiple neural networks in order to improve model long range prediction performance are compared in this paper. It is shown that combining multiple non-perfect neural networks can improve model predictions, especially long range predictions. Among the different approaches, the principal component regression based approaches generally give very good performance. Selective combination is also very beneficial to the improvement of model predictions.

Publication metadata

Author(s): Zhang J; Ahmad Z

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Control Applications

Year of Conference: 2002

Pages: 966-971

ISSN: 1085-1992

Publisher: IEEE


DOI: 10.1109/CCA.2002.1038733

Library holdings: Search Newcastle University Library for this item

ISBN: 0780373863