Kalman Filtering and Neural Networks P6

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Kalman Filtering and Neural Networks P6

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LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM Sam Roweis and Zoubin Ghahramani Gatsby Computational Neuroscience Unit, University College London, London U.K. (zoubin@gatsby.ucl.ac.uk) 6.1 LEARNING STOCHASTIC NONLINEAR DYNAMICS Since the advent of cybernetics, dynamical systems have been an important modeling tool in fields ranging from engineering to the physical and social sciences. Most realistic dynamical systems models have two essential features. First, they are stochastic – the observed outputs are a noisy function of the inputs, and the dynamics itself may be driven by some unobserved noise process. Second, they can be characterized by Kalman Filtering and Neural Networks, Edited by Simon Haykin...

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