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Lookup NU author(s): Zainal Ahmad, Dr Jie ZhangORCiD
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Combining multiple neural networks appears to be a very promising approach for improving neural network generalization since it is very difficult, if not impossible, to develop a perfect single neural network. Therefore in this paper, a nonlinear model predictive control (NMPC) strategy using multiple neural networks is proposed. Instead of using a single neural network as a model, multiple neural networks are developed and combined to model the nonlinear process and then used in NMPC. The proposed technique is applied to water level control in a conic water tank. Application results demonstrate that the proposed technique can significantly improve both setpoint tracking and disturbance rejection performance. © Springer-Verlag Berlin Heidelberg 2006.
Author(s): Ahmad Z, Zhang J
Editor(s): Wang, J; Yi, Z; Zurada, JM; Lu, B-L; Hujun, Y
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Advances in Neural Networks (ISNN): Third International Symposium on Neural Networks
Year of Conference: 2006
Pages: 943-948
ISSN: 0302-9743 (Print) 1611-3349 (Online)
Publisher: Springer Berlin / Heidelberg
URL: http://dx.doi.org/10.1007/11760023_139
DOI: 10.1007/11760023_139
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783540344377