Data-Driven Synthesis of Symbolic Abstractions With Guaranteed Confidence
Author:
Affiliation:
1. Institute for Dynamic Systems and Control, ETH Zurich, Zürich, Switzerland
Funder
Swiss National Science Foundation under NCCR Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Control and Optimization,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/7782633/9828546/09805765.pdf?arnumber=9805765
Reference21 articles.
1. Compositional synthesis of finite abstractions for networks of systems: A small-gain approach
2. From model-based control to data-driven control: Survey, classification and perspective
3. Data-Driven Synthesis of Symbolic Abstractions With Guaranteed Confidence
4. Learning-based symbolic abstractions for nonlinear control systems;hashimoto;arXiv 2004 01879,2020
5. Symbolic Abstractions From Data: A PAC Learning Approach
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