Model Learning
The state-of-the-art diagnosis and estimation algorithms rely on hybrid continuous and discrete system models, but defining these models is a significant challenge. Furthermore, system dynamics may change; hence acquiring hybrid models from observed data is an appealing approach. While there is a rich body of research on system identification for continuous dynamic systems, hybrid model learning remains an open problem. My research has developed a solution to this problem for a restricted class of stochastic hybrid systems, described here.