THE EM ALGORITHM AND NEURAL NETWORKS FOR NONLINEAR STATE SPACE ESTIMATION
J.F.G. de Freitas, M. Niranjan and A.H. Gee
In this paper, we derive the EM algorithm for nonlinear state space models. We show how this algorithm, in conjunction with the well known techniques of Kalman smoothing, can be used for nonlinear system identification. A multi-layer perceptron, whose derivatives are computed by back-propagation, is used to generate the measurements mapping. We find that the method is intrinsically very powerful, simple, elegant and stable. However, it exhibits very slow convergence.
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