A fast input selection algorithm for neural modeling of nonlinear dynamic systems

Authors:
Kang Li;Jian Xun Peng
Affiliations:
School of Electrical & Electronic Engineering, Queen's University Belfast, Belfast, UK;School of Electrical & Electronic Engineering, Queen's University Belfast, Belfast, UK
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 2
Cited 0

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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.