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

Khandelwal, Dhruv

9783030903459: Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach (Springer Theses)

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ISBN 10: 3030903451 ISBN 13: 9783030903459

Editorial: Springer, 2023

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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.

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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.

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EditorialSpringer
A�o de publicaci�n2023
ISBN 10 3030903451
ISBN 13 9783030903459
Encuadernaci�nTapa blanda
IdiomaIngl�s
N�mero de edici�n1
N�mero de p�ginas256
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Automating Data-Driven Modelling of Dynamical Systems

Dhruv Khandelwal

Publicado por Springer International Publishing, 2023

ISBN 10: 3030903451 ISBN 13: 9783030903459

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Kartoniert / Broschiert. 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: 795423258

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Automating Data-Driven Modelling of Dynamical Systems

Dhruv Khandelwal

Publicado por Springer International Publishing Feb 2023, 2023

ISBN 10: 3030903451 ISBN 13: 9783030903459

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Taschenbuch. 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: 9783030903459

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

Dhruv Khandelwal

Publicado por Springer International Publishing, 2023

ISBN 10: 3030903451 ISBN 13: 9783030903459

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Taschenbuch. 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: 9783030903459

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

Khandelwal, Dhruv

Publicado por Springer, 2023

ISBN 10: 3030903451 ISBN 13: 9783030903459

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Condici�n: New. In. N� de ref. del art�culo: ria9783030903459_new

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Automating Data-Driven Modelling of Dynamical Systems

Dhruv Khandelwal

Publicado por Springer International Publishing, Springer International Publishing Feb 2023, 2023

ISBN 10: 3030903451 ISBN 13: 9783030903459

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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania

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Taschenbuch. 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: 9783030903459

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