TY - GEN
T1 - A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems
AU - Yang, Yejiang
AU - Mo, Zihao
AU - Xiang, Weiming
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - data-driven modeling
KW - hybrid automata
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85163369716&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163369716&partnerID=8YFLogxK
U2 - 10.1109/ICIT58465.2023.10143031
DO - 10.1109/ICIT58465.2023.10143031
M3 - Conference contribution
AN - SCOPUS:85163369716
T3 - Proceedings of the IEEE International Conference on Industrial Technology
BT - 2023 IEEE International Conference on Industrial Technology, ICIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Industrial Technology, ICIT 2023
Y2 - 4 April 2023 through 6 April 2023
ER -