[Zurück]

S. Winkler, A. Körner, F. Breitenecker:
"Neural Network Application for Event Detection in Hybrid Dynamical Systems";
in: "Proceedings of 25. ASIM Symposium Simulationstechnik 2020", 25; C. Deatcu, D. Lückerath, O. Ullrich, U. Durak (Hrg.); ARGESIM / ASIM, ARGE Simulation News (ARGESIM), 2020, ISBN: 978-3-901608-93-3, S. 121 - 127.

This contribution investigates a feed-forward neural networt approach for event detection in hybrid dynamical models. machine learning algrithms are commonly used in software development. In recent years these approaches have also been increasingly applied in modelling and simulation of physical systems. A significant amount of these models use artifical neural networks. However, hybrid dynamical systems describe a combination of different methods to describe a continuous process, which experiences behavioural changes at discrete events. Accordingly, the models of such systems are based on a combination of discrete and continuous methods and are often illustrated as automation. Based on these two areas an approach, to predict the event time of the discrete processes, is presented.

http://dx.doi.org/10.11128/arep.59.a59017