Verification of machine learning based cyber-physical systems: a comparative study

Verification of machine learning based cyber-physical systems: a comparative study
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基于机器学习的网络物理系统的验证:比较研究

DOI:
10.1145/3501710.3519540
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发表时间:
2022
期刊:
Proceedings of the 25th ACM International Conference on Hybrid Systems: Computation and Control
影响因子:
--
通讯作者:
C. Pagetti
C. Pagetti
中科院分区:
--
文献类型:
--
作者:
Arthur Clavière;Laura Altieri Sambartolomé;E. Asselin;C. Garion;C. Pagetti

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在本文中,我们对现有的验证网络物理系统安全性的形式化方法与基于机器学习的控制器进行了比较。我们重点研究了一种特殊形式的基于机器学习的控制器,即基于多神经网络的分类器,其体系结构对于嵌入式应用特别感兴趣。我们比较了精确和近似验证技术,基于几个真实世界的基准,例如无人驾驶飞行器的防撞系统。
In this paper, we conduct a comparison of the existing formal methods for verifying the safety of cyber-physical systems with machine learning based controllers. We focus on a particular form of machine learning based controller, namely a classifier based on multiple neural networks, the architecture of which is particularly interesting for embedded applications. We compare both exact and approximate verification techniques, based on several real-world benchmarks such as a collision avoidance system for unmanned aerial vehicles.
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