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Audiobooks & Video Training Nonlinear state and parameter estimation of spatially distributed systems
Posted on 2010-11-24
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More Felix Sawo, "Nonlinear state and parameter estimation of spatially distributed systems" Abstract: In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion. NO MIRORS!!!
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