This paper investigates the filter design problem for linear time-invariant dynamic systems when no mathematical model is available, but a set of initial experiments can be performed where also the variable to be estimated is measured. Two-step and direct approaches are considered within both a stochastic and a deterministic framework and optimal or suboptimal solutions are reviewed.

From Model-Based to Data-Driven Filter Design / Milanese, Mario; RUIZ PALACIOS, FREDY ORLANDO; Taragna, Michele (MODELING AND SIMULATION IN SCIENCE, ENGINEERING AND TECHNOLOGY). - In: Bounded Noises in Physics, Biology, and Engineering. Modeling and Simulation in Science, Engineering and Technology / d'Onofrio A.; ed.. - STAMPA. - Boston : Birkhäuser, 2013. - ISBN 978-1-4614-7384-8. - pp. 273-285 [10.1007/978-1-4614-7385-5_16]

From Model-Based to Data-Driven Filter Design

MILANESE, Mario;RUIZ PALACIOS, FREDY ORLANDO;TARAGNA, MICHELE
2013

Abstract

This paper investigates the filter design problem for linear time-invariant dynamic systems when no mathematical model is available, but a set of initial experiments can be performed where also the variable to be estimated is measured. Two-step and direct approaches are considered within both a stochastic and a deterministic framework and optimal or suboptimal solutions are reviewed.
978-1-4614-7384-8
Bounded Noises in Physics, Biology, and Engineering. Modeling and Simulation in Science, Engineering and Technology
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2518614
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