A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems
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Abstract (arXiv)
In this paper, a computationally efficient data-driven hybrid automaton model is proposed to capture unknown complex dynamical system behaviors using multiple neural networks. The sampled data of the system is divided by valid partitions into groups corresponding to their topologies and based on which, transition guards are defined. Then, a collection of small-scale neural networks that are computationally efficient are trained as the local dynamical description for their corresponding topologies. After modeling the system with a neural-network-based hybrid automaton, the set-valued reachability analysis with low computation cost is provided based on interval analysis and a split and combined process. At last, a numerical example of the limit cycle is presented to illustrate that the developed models can significantly reduce the computational cost in reachable set computation without sacrificing any modeling precision.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 26 Apr 2023.
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References · 9
- 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 445
- 2019Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networkscited by 293
- 2021Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcited by 228
- 2020NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systemscited by 128
- 2017Output Reachable Set Estimation and Verification for Multi-Layer Neural Networkscited by 80
- 2020Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approachcited by 36
- 2021Artificial neural network as a universal model of nonlinear dynamical systemscited by 4
- 2020Learnability and Robustness of Shallow Neural Networks Learned With a Performance-Driven BP and a Variant PSO For Edge Decision-Makingcited by 2
- 2021Robust Optimization Framework for Training Shallow Neural Networks Using Reachability Methodcited by 1
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