Verification Approaches for Learning-Enabled Autonomous Cyber–Physical Systems

Hoang-Dung Tran,Taylor T Johnson,Weiming Xiang

doi:10.1109/mdat.2020.3015712

Verification Approaches for Learning-Enabled Autonomous Cyber–Physical Systems

Hoang-Dung Tran, Taylor T Johnson + Show 1 more

https://doi.org/10.1109/mdat.2020.3015712

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Journal: IEEE design & test	Publication Date: Aug 12, 2020
Citations: 10

Affiliation: University of Nebraska–Lincoln, Vanderbilt University, Augusta University

#Learning Enabled Components #Autonomy In Cyber-physical Systems + Show 8 more

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Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes:</i> Neural network control systems are often at the heart of autonomous systems. The authors classify existing verification methods for these systems and advocate the necessity of integrating verification techniques in the training process to enhance robustness. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Selma Saidi, TU Dortmund</i>

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