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Lookup NU author(s): Dr Jie ZhangORCiD
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Since it is generally difficult, if not impossible, to develop a perfect neural network, a single neural network can lack reliability. Therefore a single neural network based fault diagnosis system may not give reliable fault diagnosis. Neural network model reliability or robustness can be improved by combining several non-perfect neural networks. Each individual network is trained on a bootstrap re-sample of the original training data. The outputs from the individual networks are averaged to give the final diagnosis results. Applications of the proposed method to a continuous stirred tank reactor demonstrate that a stacked neural network can give more reliable diagnosis than a single neural network.
Author(s): Zhang J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Conference on Control Applications
Year of Conference: 2002
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