System Identification of Nonlinear State-Space Models, Report no. LiTH-ISY-R-2977
نویسندگان
چکیده
This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More speci cally, 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 provide arbitrarily good estimates. The maximisation (M) step is solved using standard techniques from numerical optimisation theory. Simulation examples demonstrate the e cacy of our proposed solution.
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