Identification of Hybrid Systems with Particle Filtering and Expectation Maximization

This talk addresses the problem of parameter identification for a class of hybrid systems with continuous states and discrete time-varying parameters that can take different values from a finite set at each time instance. The identification of such systems typically results in non-convex formulation. Although these problems can be solved as a mixed integer program, the resulting complexity may be intractable. Another approach involves heuristics in order to deliver approximate solutions. An offline (batch) algorithm is introduced, that combines Particle Filter and Expectation Maximization for the identification of such systems. The performance of the method is demonstrated on simulated systems, and on experimental diauxic bacterial growth data.

András Hartmann

András Hartmann received his MSc degree in Information Systems and Computational Engineering from Budapest University of Technology and Economics (BUTE) in 2005 and in Biomedical Engineering from BUTE and Semmelweis Medical University in 2008, respectively. Since July 2009 he is a member of the INESC-ID KDBIO Group, since 2011 he is a PhD student on Instituto Superior Técnico. His main interests are dynamic modeling and parameter identification algorithms such as filtering techniques, identification of parameter-varying systems and hybrid models. He is interested in biological applications, such as metabolic networks; spatial and temporal connectivity in the brain; dynamic modeling of cardiovascular measurements and Human Immunodeficiency Virus (HIV) intra-host infection.INESC-ID, IST