Learning a behavior model of hybrid systems through combining model-based testing and machine learning
BK Aichernig, R Bloem, M Ebrahimi, M Horn… - Testing Software and …, 2019 - Springer
Abstract Models play an essential role in the design process of cyber-physical systems. They
form the basis for simulation and analysis and help in identifying design problems as early
as possible. However, the construction of models that comprise physical and digital behavior
is challenging. Therefore, there is considerable interest in learning such hybrid behavior by
means of machine learning which requires sufficient and representative training data
covering the behavior of the physical system adequately. In this work, we exploit a …
form the basis for simulation and analysis and help in identifying design problems as early
as possible. However, the construction of models that comprise physical and digital behavior
is challenging. Therefore, there is considerable interest in learning such hybrid behavior by
means of machine learning which requires sufficient and representative training data
covering the behavior of the physical system adequately. In this work, we exploit a …
Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
BK Aichernig, R Bloem, M Ebrahimi, M Horn… - arXiv preprint arXiv …, 2019 - arxiv.org
Models play an essential role in the design process of cyber-physical systems. They form the
basis for simulation and analysis and help in identifying design problems as early as
possible. However, the construction of models that comprise physical and digital behavior is
challenging. Therefore, there is considerable interest in learning such hybrid behavior by
means of machine learning which requires sufficient and representative training data
covering the behavior of the physical system adequately. In this work, we exploit a …
basis for simulation and analysis and help in identifying design problems as early as
possible. However, the construction of models that comprise physical and digital behavior is
challenging. Therefore, there is considerable interest in learning such hybrid behavior by
means of machine learning which requires sufficient and representative training data
covering the behavior of the physical system adequately. In this work, we exploit a …
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