[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.
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Temporal machine learning for switching control
Publication date :
2000
Event name :
4th European Conference on Principles of Data Mining and Knowledge Discovery
Event place :
Lyon, France
Audience :
International
Main work title :
Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery
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