System identification using selforganizing feature maps
Witkosski U, Ruping S, Rückert U, Schutte F, Beineke S, Grotstollen H (1997)
In: Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). IEE: 100-105.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Autor*in
Witkosski, U.;
Ruping, S.;
Rückert, UlrichUniBi;
Schutte, F.;
Beineke, S.;
Grotstollen, H.
Abstract / Bemerkung
A method for identification of mechanical systems is reported. The identification of mechanical systems is often done by neural networks used as black boxes in order to produce an inverse system model for control. Contrary to this approach, we intend to identify the mechanical structure and parameters, which allows the use of conventional control theory. The basis of the identification system is a self-organizing feature map (SOFM) representing the systems to be identified. The systems are described by their response to test signals, which are used for feature extraction. The extracted features are analyzed with SOFMs to explore the feature space. The map is well suited for this kind of interpretation. As an application example, the identification of a two mass system is presented
Stichworte
inverse system model;
mechanical systems;
parameter identification;
neural networks;
system identification;
self organizing feature maps;
test signals;
control theory;
SOFM;
feature extraction;
identification;
two mass system
Erscheinungsjahr
1997
Titel des Konferenzbandes
Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440)
Seite(n)
100-105
ISBN
0852966903
ISSN
0537-9989
Page URI
https://pub.uni-bielefeld.de/record/2286012
Zitieren
Witkosski U, Ruping S, Rückert U, Schutte F, Beineke S, Grotstollen H. System identification using selforganizing feature maps. In: Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). IEE; 1997: 100-105.
Witkosski, U., Ruping, S., Rückert, U., Schutte, F., Beineke, S., & Grotstollen, H. (1997). System identification using selforganizing feature maps. Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440), 100-105. IEE. https://doi.org/10.1049/cp:19970709
Witkosski, U., Ruping, S., Rückert, Ulrich, Schutte, F., Beineke, S., and Grotstollen, H. 1997. “System identification using selforganizing feature maps”. In Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440), 100-105. IEE.
Witkosski, U., Ruping, S., Rückert, U., Schutte, F., Beineke, S., and Grotstollen, H. (1997). “System identification using selforganizing feature maps” in Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440) (IEE), 100-105.
Witkosski, U., et al., 1997. System identification using selforganizing feature maps. In Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). IEE, pp. 100-105.
U. Witkosski, et al., “System identification using selforganizing feature maps”, Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440), IEE, 1997, pp.100-105.
Witkosski, U., Ruping, S., Rückert, U., Schutte, F., Beineke, S., Grotstollen, H.: System identification using selforganizing feature maps. Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). p. 100-105. IEE (1997).
Witkosski, U., Ruping, S., Rückert, Ulrich, Schutte, F., Beineke, S., and Grotstollen, H. “System identification using selforganizing feature maps”. Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). IEE, 1997. 100-105.
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