Data-Driven Abstractions With Probabilistic Guarantees for Linear PETC Systems

• DOI: 10.1109/lcsys.2022.3186187
• Publication Date: 2022-06-24T19:39:49Z
• Authors: Andrea Peruffo; Manuel Mazo
• Keywords: FOS: Computer and information sciences; 0209 industrial biotechnology; Support vector machines; Discrete event systems; Computer Science - Artificial Intelligence; Formal Languages and Automata Theory (cs.FL); 005; Stability analysis; Computational modeling; Computer Science - Formal Languages and Automata Theory; Systems and Control (eess.SY); 02 engineering and technology; Electrical Engineering and Systems Science - Systems and Control; Automata; Statistical learning; Behavioral sciences; Artificial Intelligence (cs.AI); FOS: Electrical engineering, electronic engineering, information engineering; Picture archiving and communication systems; Probabilistic logic

We employ the scenario approach to compute probably approximately correct (PAC) bounds on average inter-sample time (AIST) generated by an unknown PETC system, based a finite number of samples. extend optimisation multiclass SVM algorithms in order construct PAC map between concrete state-space and times. then build traffic model applying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> -complete relation find, underlying graph, cycles minimum maximum weight: these provide lower upper AIST. Numerical benchmarks show practical applicability our method, which is compared against model-based state-of-the-art tools.