Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach (Springer Theses)

Khandelwal, Dhruv

ISBN 10: 3030903427 ISBN 13: 9783030903428

Editorial: Springer, 2022

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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.

Acerca del autor: Charlotte y Peter Fiell son dos autoridades en historia, teor�a y cr�tica del dise�o y han escrito m�s de sesenta libros sobre la materia, muchos de los cuales se han convertido en �xitos de ventas. Tambi�n 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, Dise�o del siglo XX, El dise�o industrial de la A a la Z, Scandinavian Design y Dise�o del siglo XXI.

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T�tulo: Automating Data-Driven Modelling of ...
Editorial: Springer
A�o de publicaci�n: 2022
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Condici�n: New

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Automating Data-driven Modelling of Dynamical Systems

Dhruv Khandelwal

Publicado por Springer International Publishing, 2022

ISBN 10: 3030903427 ISBN 13: 9783030903428

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Condici�n: 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. N� de ref. del art�culo: 512678817

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Automating Data-Driven Modelling of Dynamical Systems | An Evolutionary Computation Approach

Dhruv Khandelwal

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Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach (Springer Theses)

Khandelwal, Dhruv

Publicado por Springer, 2022

ISBN 10: 3030903427 ISBN 13: 9783030903428

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Automating Data-Driven Modelling of Dynamical Systems

Dhruv Khandelwal

Publicado por Springer International Publishing, Springer International Publishing Feb 2022, 2022

ISBN 10: 3030903427 ISBN 13: 9783030903428

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Buch. Condici�n: 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. N� de ref. del art�culo: 9783030903428

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Automating Data-Driven Modelling of Dynamical Systems : An Evolutionary Computation Approach

Dhruv Khandelwal

Publicado por Springer International Publishing, 2022

ISBN 10: 3030903427 ISBN 13: 9783030903428

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Buch. Condici�n: 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. N� de ref. del art�culo: 9783030903428

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Automating Data-Driven Modelling of Dynamical Systems

Dhruv Khandelwal

Publicado por Springer International Publishing Feb 2022, 2022

ISBN 10: 3030903427 ISBN 13: 9783030903428

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

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Buch. Condici�n: 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. N� de ref. del art�culo: 9783030903428

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