Abstract
In the present work a sliding window approach for the Levenberg-Marquardt algorithm is used for on-line modelling a time variant system. The system used is a first order cruise control in which a modification is introduced to change the system gain at some point of operation. The initial control of the cruise control is performed by a PI not particularly optimised but enough to keep the system working within the intended range, which is then replaced by an Artificial Neural Network as soon as it is trained, using an Internal Model Controller loop.
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Dias, F.M., Antunes, A., Vieira, J., Mota, A.M. (2006). On-Line Learning of a Time Variant System. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_97
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DOI: https://doi.org/10.1007/11779568_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
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