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Improving Data based Nonlinear Process Modelling through Bayesian Combination of Multiple Neural Networks

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.

Publication metadata

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


DOI: 10.1109/IJCNN.2003.1223952

Library holdings: Search Newcastle University Library for this item

ISBN: 0780378989