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System identification of nonlinear state-space models
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DOI：10.1016/j.automatica.2010.10.013.png)
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Abstract
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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. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
System identification
Nonlinear models
Dynamic systems
Monte Carlo method
Smoothing filters
Expectation maximisation algorithm
Particle methods
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