Polynomial Neural Barrier Certificate Synthesis of Hybrid Systems via Counterexample Guidance

This article presents a novel approach to the safety verification of hybrid systems by synthesizing neural barrier certificates (BCs) via counterexample-guided neural network (NN) learning combined with sum-of-square (SOS)-based verification. We learn more easily verifiable BCs with NN polynomial ex...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems 2024-11, Vol.43 (11), p.3756-3767
Hauptverfasser:	Zhao, Hanrui, Liu, Banglong, Dehbi, Lydia, Xie, Huijiao, Yang, Zhengfeng, Qian, Haifeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Counterexample guidance Generators hybrid system Hybrid systems Learning Linear matrix inequalities Neural networks Optimization polynomial neural barrier certificate Polynomials Programming Safety safety verification Scalability Synthesis Testing Training Verification
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