Verification Approaches for Learning-Enabled Autonomous Cyber-Physical Systems

被引：9

作者：

Tran, Hoang-Dung ^{[1
]}

Xiang, Weiming ^{[2
]}

Johnson, Taylor T. ^{[3
]}

机构：

[1] Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA

[2] Augusta Univ, Sch Comp & Cyber Sci, Augusta, GA USA

[3] Vanderbilt Univ, Elect Engn & Comp Sci, 221 Kirkland Hall, Nashville, TN 37212 USA

来源：

IEEE DESIGN & TEST | 2022年 / 39卷 / 01期

关键词：

Biological neural networks; Neurons; Safety; Encoding; Formal verification; Machine learning; Cyber-physical systems; verification; machine learning; autonomy; cyber-physical systems; NETWORKS;

D O I：

10.1109/MDAT.2020.3015712

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Editor's notes: 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. -Selma Saidi, TU Dortmund

引用

页码：24 / 34

页数：11

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