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.
Author: | Hao Wu, Frank Noe |
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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 |