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Elsevier, Automatica, 1(47), p. 39-49

DOI: 10.1016/j.automatica.2010.10.013

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System identification of nonlinear state-space models

Journal article published in 2011 by Thomas B. Schön ORCID, Adrian Wills, Brett Ninness
This paper is available in a repository.
This paper is available in a repository.

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Abstract

This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the states are required. This problem lends itself perfectly to the particle smoother, which provides arbitrarily good estimates. The maximisation (M) step is solved using standard techniques from numerical optimisation theory. Simulation examples demonstrate the efficacy of our proposed solution.