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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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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DOI: https://doi.org/10.1007/11538059_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
Online ISBN: 978-3-540-31902-3
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