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ISBN 10: 3866443706
ISBN 13: 978-3866443709
Author: Felix Sawo
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.
nonlinear state and parameter estimation
nonlinear state estimation
nonlinear parameter estimation
nonlinear parameter estimation through particle swarm optimization
nonlinear state-space model example
Tags: Felix Sawo, Nonlinear, State, Parameter, Estimation, Spatially Distributed, Systems
