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Lookup NU author(s): Qiusha Zhou, Dr Zhihua Xiong, Dr Jie ZhangORCiD
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A two-layer hierarchical neural network is proposed to predict the product qualities of an industrial KTI GK-V ethylene pyrolysis process. The first layer of the model is used to classify these changes into different operating conditions. In the second layer, the process under each operating condition is modeled using bootstrap aggregated neural networks (BANN) with sequential training algorithm. The overall output is obtained by combining all the trained networks. Results of application to the actual process show that the proposed soft-sensing model possesses good generalization capability. © Springer-Verlag Berlin Heidelberg 2006.
Author(s): Zhou Q, Xiong Z, Zhang J, Xu Y
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: 1132-1137
ISSN: 0302-9743 (Print) 1611-3349 (Online)
Publisher: Springer Berlin / Heidelberg
URL: http://dx.doi.org/10.1007/11760191_165
DOI: 10.1007/11760191_165
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783540344827