Awry czuk (20 risultati)
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Da: Homeless Books, Berlin, GermaniaHomeless Books
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Hardcover. Condizione: Sehr gut. Unread book in excellent condition. Language - English. Ships from Berlin.
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Taschenbuch. Condizione: Neu. Computationally Efficient Model Predictive Control Algorithms | A Neural Network Approach | Maciej ¿Awry¿Czuk | Taschenbuch | Studies in Systems, Decision and Control | xxiv | Englisch | 2016 | Springer | EAN 9783319350219 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17,… 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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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 - This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation of the m…odel or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 106,99
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation of the model or… of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.
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Lingua: Inglese
Editore: De Gruyter, 2022
Serie: Libro 10 di 10 - Fractional Calculus in Applied Sciences and Engineering
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Pawel D. Domanski, Maciej Lawrynczuk, Warsaw University of Technology, Poland YangQuan Chen, Mesa Lab, University of California, USA.Outliers play an important, though underestimated..
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Lingua: Inglese
Editore: Springer, 2022
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Nonlinear Predictive Control Using Wiener Models | Computationally Efficient Approaches for Polynomial and Neural Structures | Maciej ¿Awry¿Czuk | Taschenbuch | Studies in Systems, Decision and Control | xxiii | Englisch | 2022 | Springer | EAN 9783030838171 | Verantwortliche Person für die EU: Spri…nger Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Lingua: Inglese
Editore: De Gruyter GmbH, 2022
Serie: Libro 10 di 10 - Fractional Calculus in Applied Sciences and Engineering
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Da: preigu, Osnabrück, Germaniapreigu
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Buch. Condizione: Neu. Outliers in Control Engineering | Fractional Calculus Perspective, Fractional Calculus in Applied Sciences and Engineering 10 | YangQuan Chen, Maciej ?awry?czuk Pawe? D. Doma?ski | Buch | X | Englisch | 2022 | De Gruyter GmbH | EAN 9783110729078 | Verantwortliche Person für die EU: Walter de Gruyter GmbH,…De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu.
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Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2022
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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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 - This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlineari…ty. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant.A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.
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Lingua: Inglese
Editore: Springer, 2021
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unf…ortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant.A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 340 | Sprache: Englisch | Produktart: Bücher | This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include:· A few types of suboptimal MPC algorithms in which a linear approxi…mation of the model or of the predicted trajectory is successively calculated on-line and used for prediction.· Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models.· The MPC algorithms based on neural multi-models (inspired by the idea of predictive control).· The MPC algorithms with neural approximation with no on-line linearization.· The MPC algorithms with guaranteed stability and robustness.· Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.
Lingua: Inglese
Editore: Palgrave Macmillan, 2022
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 368 | Sprache: Englisch | Produktart: Bücher | This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence… of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.
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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 -This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approx…imation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding. 340 pp. Englisch.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation… of the model or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding. 340 pp. Englisch.
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Da: preigu, Osnabrück, Germaniapreigu
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Buch. Condizione: Neu. Computationally Efficient Model Predictive Control Algorithms | A Neural Network Approach | Maciej ¿Awry¿Czuk | Buch | Studies in Systems, Decision and Control | xxiv | Englisch | 2014 | Springer | EAN 9783319042282 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelb…erg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation of…the model or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 340 pp. Englisch.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 106,99
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximat…ion of the model or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 340 pp. Englisch.
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Lingua: Inglese
Editore: Springer International Publishing Sep 2022, 2022
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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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 -This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of pr…ocess nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant.A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages of neural Wiener models are demonstrated. 368 pp. Englisch.
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Lingua: Inglese
Editore: Springer International Publishing Sep 2021, 2021
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 160,49
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process n…onlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant.A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages of neural Wiener models are demonstrated. 368 pp. Englisch.
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Lingua: Inglese
Editore: Springer, Springer Sep 2021, 2021
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Introduction to Model Predictive Control.- MPC Algorithms Using Input-Output Wiener Models.- MPC Algorithms Using State-Space Wiener Models.- Conclusions.- Index.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 368 pp. Englisch.
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Lingua: Inglese
Editore: Springer, Springer Sep 2022, 2022
Serie: Libro 346 di 378 - Studies in Systems, Decision and Control
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- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 160,49
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Introduction to Model Predictive Control.- MPC Algorithms Using Input-Output Wiener Models.- MPC Algorithms Using State-Space Wiener Models.- Conclusions.- Index.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 368 pp. Englisch.
