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Abstract
In neural modeling of non-linear dynamic systems, the neural inputs can include any system variable with time delays. To obtain the optimal subset of inputs regarding a performance measure is a combinational problem, and the selection process can be very time-consuming. In this paper, neural input selection is transformed into a model selection problem and a new fast input selection method is used. This method is then applied to the neural modeling of a continuous stirring tank reactor (CSTR) to confirm its effectiveness.
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Authors and Affiliations
School of Electrical & Electronic Engineering, Queen’s University Belfast, Ashby Building, Stranmillis Road, Belfast, BT9 5AH, UK
Kang Li & Jian Xun Peng
- Kang Li
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- Jian Xun Peng
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Editors and Affiliations
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences,, China
De-Shuang Huang
School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China
Xiao-Ping Zhang
School of Electrical and Electronic Engineering, Nanyang Technological University, P.O. Box, Singapore
Guang-Bin Huang
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, K., Peng, J.X. (2005). A Fast Input Selection Algorithm for Neural Modeling of Nonlinear Dynamic Systems. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_108
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