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Abstract: | We provide a novel solution to the problem of simultaneously estimating the unknown parameters and hidden states of a nonlinear dynamical system. Our solution is based on the expectation maximization (EM) algorithm, an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables [1]. EM has been applied to system identification in linear statespace models, where the state variables are hidden from the observer and both the state and the... (Update)
Context of citations to this paper: More
.... factor analysis model has been considered easier than the problem of estimating a nonlinear factor analysis model without dynamics [11]. Given the success of the nonlinear factor analysis algorithm proposed in [1] to estimate factors of dimensionality up to ten, it is natural...
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BibTeX entry: (Update)
S. T. Roweis and Z. Ghahramani, "An EM algorithm for identification of nonlinear dynamical systems," in Kalman Filtering and Neural Networks, S. Haykin, ed. To appear. http://citeseer.ist.psu.edu/306925.html More
@misc{ roweis-em,
author = "S. Roweis and Z. Ghahramani",
title = "An EM algorithm for identification of nonlinear dynamical systems",
text = "S. T. Roweis and Z. Ghahramani, An EM algorithm for identification of nonlinear
dynamical systems, in Kalman Filtering and Neural Networks, S. Haykin, ed.
To appear.",
url = "citeseer.ist.psu.edu/306925.html" }
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