The distinction between off-line(nonrecursive) and on-line(recursive) methods is not as rigid as it might seem. The work of Kalman provided a recursive solution to the minimum variance estimation problem for linear systems with Gaussian variables. The Kalman filter and its equivalence to least-squares is known and the moving horizon approach is formulated as a particular case of both of the methods. The incorporation of prior knowledge of the unknown variable ranges as inequality constraints is discussed. A real time flood forecasting application of the approach is shown and the relevance of the horizon-size is discussed.
A Moving Horizon Based Approach for Real Time Flood Forecasting
SALTALIPPI, Carla;
1998
Abstract
The distinction between off-line(nonrecursive) and on-line(recursive) methods is not as rigid as it might seem. The work of Kalman provided a recursive solution to the minimum variance estimation problem for linear systems with Gaussian variables. The Kalman filter and its equivalence to least-squares is known and the moving horizon approach is formulated as a particular case of both of the methods. The incorporation of prior knowledge of the unknown variable ranges as inequality constraints is discussed. A real time flood forecasting application of the approach is shown and the relevance of the horizon-size is discussed.File in questo prodotto:
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