E101-03 - Forschungsbereich Scientific Computing and Modelling
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Journal:
Nonlinear Analysis: Hybrid Systems
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ISSN:
1751-570X
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Date (published):
Nov-2021
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Number of Pages:
29
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Peer reviewed:
Yes
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Keywords:
Computer Science Applications; Control and Systems Engineering; Analysis; Neural networks; Hybrid dynamical systems; Modelling benchmark; Hybrid neural networks; Hybrid modelling framework
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Abstract:
Many current industry branches use hybrid approaches to solve complex application problems. Over the last decades, different tools for the simulation of such hybrid systems (e.g. Hysdel and YAMLIP) as well as the identification of hybrid systems (e.g. HIT, MLP and OAF NN) have been developed. The framework presented in this work facilitates the integration of artificial feed-forward neural networks in the modelling process of hybrid dynamical systems (HDS).
