Isbn: 9783642439773 - predictive approaches to control of complex systems: 454 (11 risultati)

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  • Lingua: Inglese

    Editore: Springer, 2014

    3642439772 / 9783642439773

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    Condizione: New. In English.

  • Lingua: Inglese

    Editore: Springer, 2014

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    Lingua: Inglese

    Editore: Springer, 2014

    3642439772 / 9783642439773

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    Taschenbuch. Condizione: Neu. Predictive Approaches to Control of Complex Systems | Gorazd Karer (u. a.) | Taschenbuch | Studies in Computational Intelligence | xii | Englisch | 2014 | Springer | EAN 9783642439773 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Lingua: Inglese

    Editore: Springer Vieweg, 2014

    3642439772 / 9783642439773

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    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.

  • Lingua: Inglese

    Editore: Springer, 2014

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

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

  • Lingua: Inglese

    Editore: Springer, 2014

    3642439772 / 9783642439773

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    Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand

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  • Lingua: Inglese

    Editore: Springer Berlin Heidelberg Okt 2014, 2014

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems. 272 pp. Englisch.

  • Lingua: Inglese

    Editore: Springer Berlin Heidelberg, 2014

    3642439772 / 9783642439773

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    Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents control of time-delayed, unstable, multivariable processes and modelling approaches for several classes of complex systems Special emphasis is put on hybrid systems with distinct nonlinearities Treats time-varying systems and adapti.

  • Lingua: Inglese

    Editore: Springer, 2014

    3642439772 / 9783642439773

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    Da: Majestic Books, Hounslow, Regno UnitoMajestic Books

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  • Lingua: Inglese

    Editore: Springer, 2014

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  • Lingua: Inglese

    Editore: Springer, Springer Okt 2014, 2014

    3642439772 / 9783642439773

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    Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 272 pp. Englisch.