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A new input selection method for neural modeling of nonlinear complex systems

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1 Author(s)
Kang Li ; Sch. of Electr. & Electron. Eng., Queen''s Univ., Belfast, UK

In neural modeling of non-linear dynamic systems, the neural inputs may include any system inputs of interest and system outputs with various time delays. To obtain the optimal subset of inputs regarding a performance measure is a combinational problem, and the selection process can be time-consuming. In this paper, the input selection is transformed to a model structure selection problem and a new input selection method is proposed. This method is then applied to the neural modeling of a practical system, and the modeling result shows its merit.

Published in:

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on  (Volume:3 )

Date of Conference:

15-19 June 2004