Isbn: 9783540782889 - discrete-time high order neural control: trained with kalman filtering: 112 (3 risultati)

Perfeziona la tua ricerca

  • Libri (3)

a

Fascia di prezzo personalizzata (EUR)

a

  • Lingua: Inglese

    Editore: Springer, 2008

    3540782885 / 9783540782889

    • Rilegato

    Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books

    Venditore con 4 stelle
    Contatta il venditore

    Condizione: Usato - Come nuovo

    EUR 127,59

    EUR 29,49 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: 1 disponibile

    Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

  • Lingua: Inglese

    Editore: Springer, 2008

    3540782885 / 9783540782889

    • Rilegato

    Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Usato - Come nuovo

    EUR 159,35

    EUR 2,36 spedizione 
    Spedito in U.S.A.

    Quantità: 15 disponibili

    Condizione: As New. Unread book in perfect condition.

  • Lingua: Inglese

    Editore: Springer, 2008

    3540782885 / 9783540782889

    • Rilegato

    Da: Buchpark, Trebbin, GermaniaBuchpark

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Usato - Ottimo

    EUR 67,09

    EUR 105,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibile

    Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.…