Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDesign of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming
Anno di pubblicazione2007
Formato-
Autore/iAngelo Alessandri; Cristiano Cervellera; Marcello Sanguineti
Affiliazioni autoriAngelo Alessandri: Department of Production Engineering, Thermoenergetics, and Mathematical Models (DIPTEM), University of Genoa, Genova 16129, Italy Cristiano Cervellera: Institute of Intelligent Systems for Automation, National Research Council of Italy (ISSIA-CNR), Genova 16149, Italy Marcello Sanguineti: Department of Communications, Computer and System Sciences (DIST), University of Genoa, Genova 16145, Italy
Autori CNR e affiliazioni
  • CRISTIANO CERVELLERA
Lingua/e
  • inglese
AbstractA methodology to design state estimators for a class of nonlinear continuous-time dynamic systems that is based on neural networks and nonlinear programming is proposed. The estimator has the structure of a Luenberger observer with a linear gain and a parameterized (in general, nonlinear) function, whose argument is an innovation term representing the difference between the current measurement and its prediction. The problem of the estimator design consists in finding the values of the gain and of the parameters that guarantee the asymptotic stability of the estimation error. Toward this end, if a neural network is used to take on this function, the parameters (i.e., the neural weights) are chosen, together with the gain, by constraining the derivative of a quadratic Lyapunov function for the estimation error to be negative definite on a given compact set. It is proved that it is sufficient to impose the negative definiteness of such a derivative only on a suitably dense grid of sampling points. The gain is determined by solving a Lyapunov equation. The neural weights are searched for via nonlinear programming by minimizing a cost penalizing grid-point constraints that are not satisfied. Techniques based on low-discrepancy sequences are applied to deal with a small number of sampling points, and, hence, to reduce the computational burden required to optimize the parameters. Numerical results are reported and comparisons with those obtained by the extended Kalman filter are made.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da86
Pagine a96
Pagine totali-
RivistaIEEE transactions on neural networks
Attiva dal 1990 al 2011
Editore: Institute of Electrical and Electronics Engineers, - New York, NY
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1045-9227
Titolo chiave: IEEE transactions on neural networks
Titolo proprio: IEEE transactions on neural networks
Titolo abbreviato: IEEE trans. neural netw.
Titoli alternativi:
  • Institute of Electrical and Electronics Engineers transactions on neural networks
  • Transactions on neural networks
  • Neural networks
Numero volume della rivista18
Fascicolo della rivista1
DOI10.1109/TNN.2006.883015
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000243918400007)
  • Scopus (Codice:2-s2.0-33846098198)
Parole chiaveFeedforward neural networks, Lyapunov function, offline optimization, penalty function, quasi-random sequences
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISSIA — Istituto di studi sui sistemi intelligenti per l'automazione
Moduli/Attività/Sottoprogetti CNR
  • SP.P06.003.002 : Supervisione e Controllo di Sistemi ed Impianti Complessi
Progetti Europei-
Allegati
Articolo pubblicato (documento privato )
Tipo documento: application/pdf

Dati storici
I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
Area disciplinareAI, Robotics & Automatic Control
Area valutazione CIVRIngegneria industriale e informatica
Rivista ISIIEEE TRANSACTIONS ON NEURAL NETWORKS [09418J0]