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Predictive Control of Complex Stochastic Systems using Markov Chain Monte Carlo with Application to Air Traffic Control

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4 Author(s)
Lecchini, A. ; Department of Engineering, University of Leicester, UK. E-mail: al394@cam.ac.uk ; Glover, W. ; Lygeros, J. ; Maciejowski, J.

Markov chain Monte Carlo (MCMC) methods can be used to make optimal decisions in very complex situations in which stochastic effects are prominent. In this paper we briefly introduce our current research on the application of MCMC to the predictive control of complex stochastic systems and the application to air traffic control.

Published in:
Nonlinear Statistical Signal Processing Workshop, 2006 IEEE

Date of Conference: 13-15 Sept. 2006

Page(s):
175 - 178
E-ISBN :
978-1-4244-0581-7
Print ISBN:
978-1-4244-0581-7
Conference Location :
Cambridge, UK
Digital Object Identifier :
10.1109/NSSPW.2006.4378848
Product Type:
Conference Publications

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