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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. Thus combining multiple neural networks can enhance the neural network model performance. In this paper, a Bayesian combination method is developed for non-linear dynamic process modelling and compared with simple averaging. Instead of using fixed combination weights, the estimated probability of a particular network being the true model is used as the combination weight for combining that network. A nearest neighbour method is used in estimating the network error for a given input data point, which is then used in calculating the combination weights for individual networks. The prior probability is estimated using the SSE of individual networks on a sliding window covering the most recent sampling times. It is shown that Bayesian combination generally outperforms simple averaging.
Author(s): Zhang J; Ahmad Z
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Proceedings of the International Joint Conference on Neural Networks
Year of Conference: 2003
Pages: 2472-2477
ISSN: 1098-7576
Publisher: IEEE
URL: http://dx.doi.org/10.1109/IJCNN.2003.12239521098-7576
DOI: 10.1109/IJCNN.2003.1223952
Library holdings: Search Newcastle University Library for this item
ISBN: 0780378989
