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NIPS
1998
13 years 7 months ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis
CDC
2010
IEEE
210views Control Systems» more  CDC 2010»
13 years 1 months ago
Advances in moving horizon estimation for nonlinear systems
In the past decade, moving horizon estimation (MHE) has emerged as a powerful technique for estimating the state of a dynamical system in the presence of nonlinearities and disturb...
Angelo Alessandri, Marco Baglietto, Giorgio Battis...
TSMC
1998
135views more  TSMC 1998»
13 years 5 months ago
Universal stabilization using control Lyapunov functions, adaptive derivative feedback, and neural network approximators
— In this paper, the problem of stabilization of unknown nonlinear dynamical systems is considered. An adaptive feedback law is constructed that is based on the switching adaptiv...
Elias B. Kosmatopoulos
INFORMATICALT
2002
191views more  INFORMATICALT 2002»
13 years 6 months ago
Optimal Control of a Well-Stirred Bioreactor in the Presence of Stochastic Perturbations
We study the stochastic model for bioremediation in a bioreactor with ideal mixing. The dynamics of the examined system is described by stochastic differential equations. We consid...
Vadim Azhmyakov
HYBRID
2005
Springer
13 years 11 months ago
Polynomial Stochastic Hybrid Systems
This paper deals with polynomial stochastic hybrid systems (pSHSs), which generally correspond to stochastic hybrid systems with polynomial continuous vector fields, reset maps, a...
João P. Hespanha