Reference : Temporal machine learning for switching control
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/25761
Temporal machine learning for switching control
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2000
Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery
Springer-Verlag
LNAI 1910
401-408
Yes
No
International
Lyon, France
4th European Conference on Principles of Data Mining and Knowledge Discovery
Lyon
France
[en] machine learning
[en] In this paper, a temporal machine learning method is presented which is able to automatically construct rules allowing to detect as soon as possible an event using past and present measurements made on a complex system. This method can take as inputs dynamic scenarios directly described by temporal variables and provides easily readable results in the form of detection trees. The application of this method is discussed in the context of switching control. Switching (or discrete event) control of continuous systems consists in changing the structure of a system in such a way as to contreol its behavior. Given a particular discrete control switch, detection trees are applied to the induction of rules which decide based on the available measurements whether or not to operate a switch. Two practical applications are discussed in the context of electrical power systems emergency control.
http://hdl.handle.net/2268/25761
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2000/GW00a

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