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Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach (Springer Theses) - Tapa dura
Sinopsis
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user's perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.
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Acerca del autor
Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.
De la contraportada
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user's perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.
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- EditorialSpringer
- Año de publicación2022
- ISBN 10 3030903427
- ISBN 13 9783030903428
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas256
- Contacto del fabricanteno disponible
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Automating Data-driven Modelling of Dynamical Systems
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Springer International Publishing, 2022
ISBN 10: 3030903427
ISBN 13: 9783030903428
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Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a novel approach for automating system identificationOffers novel solutions to multi-criteria system identification problemsReviews fundamental concepts of system identificationThis book describes a user-friendly, evolut. Nº de ref. del artículo: 512678817
Cantidad disponible: Más de 20 disponibles
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Automating Data-Driven Modelling of Dynamical Systems
Publicado por
Springer International Publishing Feb 2022, 2022
ISBN 10: 3030903427
ISBN 13: 9783030903428
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user's perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification. 256 pp. Englisch. Nº de ref. del artículo: 9783030903428
Cantidad disponible: 2 disponibles
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Automating Data-Driven Modelling of Dynamical Systems : An Evolutionary Computation Approach
Publicado por
Springer International Publishing, 2022
ISBN 10: 3030903427
ISBN 13: 9783030903428
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user's perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification. Nº de ref. del artículo: 9783030903428
Cantidad disponible: 1 disponibles
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Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach (Springer Theses)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In. Nº de ref. del artículo: ria9783030903428_new
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Automating Data-Driven Modelling of Dynamical Systems
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Springer International Publishing, Springer International Publishing Feb 2022, 2022
ISBN 10: 3030903427
ISBN 13: 9783030903428
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Neuware -This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user¿s perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 256 pp. Englisch. Nº de ref. del artículo: 9783030903428
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Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach (Springer Theses)
Librería: California Books, Miami, FL, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: I-9783030903428
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Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach (Springer Theses)
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020032815
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