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The development of an algorithm to describe a dynamic system and to predict its future behaviour in further consequence is the aim of the present study. Non parametric models provide a general description of object dynamics and artificial neural networks (ANN), which are a very flexible and universal learning method, belong to it. However, the standard estimation procedures for ANN like Levenberg-Marquardt (LM) do not consider that data is observed and consequently is uncertain. The combination with the extended Kalman filter (EKF) enables the consideration of the uncertainty in the estimation process. The analogies between EKF and LM are discussed and thereon the advantages of the EKF are outlined.

System identification of a robot arm with extended Kalman filter and artificial neural networks