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A flat Dirichlet process switching model for Bayesian estimation of hybrid systems

Please always quote using this URN:urn:nbn:de:0296-matheon-11060
  • Hybrid systems are often used to describe many complex dynamic phenomena by combining multiple modes of dynamics into whole systems. In this paper, we present a flat Dirichlet process switching (FDPS) model that defines a prior on mode switching dynamics of hybrid systems. Compared with the classical Markovian jump system (MJS) models, the FDPS model is nonparametric and can be applied to the hybrid systems with an unbounded number of potential modes. On the other hand, the probability structure of the new model is simpler and more flexible than the recently proposed hierarchical Dirichlet process (HDP) based MJS. Furthermore, we develop a Markov chain Monte Carlo (MCMC) method for estimating the states of hybrid systems with FDPS prior. And the numerical simulations of a hybrid system in different conditions are employed to show the effectiveness of the proposed approach.

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Metadaten
Author:Hao Wu, Frank Noe
URN:urn:nbn:de:0296-matheon-11060
Referee:Christof Schütte
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2012/05/18
Release Date:2012/05/18
Institute:Research Center Matheon
Freie Universität Berlin
MSC-Classification:62-XX STATISTICS / 62Mxx Inference from stochastic processes / 62M05 Markov processes: estimation
Preprint Number:961
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