NDLI: Markov modeling of Stochastic Hybrid Systems

Content Provider	IEEE Xplore Digital Library
Author	Mathew, G. Pinto, A.
Copyright Year	2010
Description	Author affiliation: United Technologies Research Center (UTRC) Inc., Berkeley, California, USA (Mathew, G.; Pinto, A.)
Abstract	Hybrid systems are a useful abstraction for systems that have a combination of discrete and continuous dynamics. For typical examples of hybrid systems, there can be various sources of stochasticity. The source of stochasticity can be in the dynamics of the continuous states, the probabilistic switching between various modes of the system and the probabilistic resetting of the continuous state after switches. Such systems can be mathematically modeled by Discrete Time Stochastic Hybrid Systems (DTSHS). If the uncertainty in the initial condition of the stochastic hybrid system is specified by a probability distribution, it is useful to compute the probability distribution of the state of the system for some time in the future. This would allow one to quantify the probability of the system to be in an undesired or unsafe set. Such computations can be useful for probabilistic verification and validation of systems. In this paper, we discuss state space models for DTSHS and present computational methods to propagate probability distributions for DTSHS.
Starting Page	1707
Ending Page	1713
File Size	659506
Page Count	7
File Format	PDF
ISBN	9781424482153
e-ISBN	9781424482160
DOI	10.1109/ALLERTON.2010.5707122
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Switches Markov processes Probabilistic logic Probability distribution Approximation methods
Content Type	Text
Resource Type	Article

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