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This paper shows how to implement Kalman estimation (including filtering and prediction) and control, and system identification, within a neural network (NN) whose only input is a stream of noisy measurement data. The operation of the fully-integrated algorithm is illustrated by a numerical example. The resulting network is a multilayer recurrent NN that may be useful for engineering applications...
Despite a large body of literature and methods devoted to the traffic matrix estimation problem, the inference of traffic flows volume from aggregated data represents a key subject facing the evolution of next generation networks. This is a particular problem in large-scale carrier networks, for which efficient, accurate and stable methods for traffic matrix modeling and estimation are vital and challenging...
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