HyMn: Mining linear hybrid automata from input output traces of cyber-physical systems
| Autor: | Sandeep K. S. Gupta, Imane Lamrani, Ayan Banerjee |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Input/output
0209 industrial biotechnology Learning automata Estimation theory Computer science Distributed computing Cyber-physical system Parameterized complexity 030209 endocrinology & metabolism 02 engineering and technology Automaton 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine Control system Hybrid system |
| Zdroj: | ICPS |
| DOI: | 10.1109/icphys.2018.8387670 |
| Popis: | Hybrid systems are versatile in modeling the interaction between the cyber and physical components of cyber-physical control systems (CPS) such as artificial pancreas (AP). They are typically used for analysis of safety of the human centric control systems which have serious consequences of failure. As such hybrid systems are parameterized and the variables often depend on the subject on which the control system is deployed. Traditionally, control systems are initially developed using average statistical estimates of the subject specific parameters. However, such excursions may lead to suboptimal designs. In this paper, we propose HyMn, a hybrid system parameter estimation tool, where the subject specific parameters in a hybrid system are automatically learned from experimental traces of the operation of a human centric CPS control system. We apply HyMn to the AP system and show that the blood glucose control is enhanced using the learned patient specific parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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