Comparison of three methods for the identification of switched hybrid systems
This talk addresses the problem of parameter identification for switched ARX system. The identification of such systems typically results in non-convex optimization problems, where finding the globally optimal solution exhibits exponential computational complexity in the size of the input. The exponential complexity may however not be tractable even for middle size problems. Another approach involves heuristics in order to reduce the computational complexity, with the trade-off, that the estimates are only approximate solutions.
Three recently proposed open-source algorithms for switched ARX system identification are compared. We consider a modified k-means algorithm: k-LinReg, and two methods using sum-of-norms regularization: PWARX and son-em.
Statistical measures are introduced in order to quantitatively compare the performance of the different methods on a simulated one-dimensional example. The individual behavior of the methods on different generated systems is also analyzed.
Bio: András Hartmann received his MSc degree in Information Systems and Computational Engineering from Budapest University of Technology and Economics in 2005 and in Biomedical Engineering from BUTE and Semmelweis Medical University in 2008, respectively. Since 2011 he is a PhD student on Instituto Superior Técnico. He was a member of the INESC-ID KDBIO Group Between 2009 and 2015, he is a member of IDMEC - Center of Intelligent Systems since 2014.
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.