Moving-horizon state estimation for nonlinear systems using neural networks (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Moving-horizon state estimation for nonlinear systems using neural networks (Articolo in rivista) (literal)

Anno

• 2011-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1109/TNN.2011.2116803 (literal)

Alternative label

• A. Alessandri; M. Baglietto; G. Battistelli; M. Gaggero (2011)
  Moving-horizon state estimation for nonlinear systems using neural networks
  in IEEE transactions on neural networks; IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• A. Alessandri; M. Baglietto; G. Battistelli; M. Gaggero (literal)

Pagina inizio

• 768 (literal)

Pagina fine

• 780 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• Journal Q1 in Artificial Intelligence (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 22 (literal)

Rivista

• IEEE transactions on neural networks (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 13 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 5 (literal)

Note

• ISI Web of Science (WOS) (literal)
• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• 1. Department of Production Engineering, Thermoenergetics, and Mathematical Models, University of Genoa, Genova 16129, Italy 2. Department of Communications, Computer and System Sciences, University of Genoa, Genova 16145, Italy 3. Dipartimento di Sistemi e Informatica, Universita' degli Studi di Firenze, Firenze 50139, Italy 4. Institute of Intelligent Systems for Automation, National Research Council of Italy, Genova 16149, Italy (literal)

Titolo

• Moving-horizon state estimation for nonlinear systems using neural networks (literal)

Abstract

• Moving-horizon (MH) state estimation is addressed for nonlinear discrete-time systems affected by bounded noises acting on system and measurement equations by minimizing a sliding-window least-squares cost function. Such a problem is solved by searching for suboptimal solutions for which a certain error is allowed in the minimization of the cost function. Nonlinear parameterized approximating functions such as feedforward neural networks are employed for the purpose of design. Thanks to the offline optimization of the parameters, the resulting MH estimation scheme requires a reduced online computational effort. Simulation results are presented to show the effectiveness of the proposed approach in comparison with other estimation techniques. (literal)

Editore

• IEEE-Institute Of Electrical And Electronics Engineers Inc. (Editore)

Prodotto di

• Monitoraggio, Controllo e ottimizzazione di impianti e sistemi complessi (SP.P06.010.001) (Modulo)
• Institute of intelligent systems for automation (ISSIA) (Istituto)

Autore CNR

• MAURO GAGGERO (Persona)

Insieme di parole chiave

• Parole chiave di "Moving-horizon state estimation for nonlinear systems using neural networks" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute of intelligent systems for automation (ISSIA) (Istituto)
• Monitoraggio, Controllo e ottimizzazione di impianti e sistemi complessi (SP.P06.010.001) (Modulo)

Autore CNR di

• MAURO GAGGERO (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• IEEE transactions on neural networks (Rivista)

Editore di

• IEEE-Institute Of Electrical And Electronics Engineers Inc. (Editore)

Insieme di parole chiave di

• Parole chiave di "Moving-horizon state estimation for nonlinear systems using neural networks" (Insieme di parole chiave)