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Automating data-driven modelling of ...

Khandelwal, Dhruv.

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Automating data-driven modelling of dynamical systems = an evolutionary computation approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automating data-driven modelling of dynamical systems/ by Dhruv Khandelwal.
Reminder of title:	an evolutionary computation approach /
Author:	Khandelwal, Dhruv.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xxiii, 229 p. :ill. (some col.), digital ;24 cm.
Notes:	"Doctoral thesis accepted by Eindhoven University of Technology, Eindhoven, The Netherlands."
[NT 15003449]:	Introduction -- The State-of-the-art -- Preliminaries - Evolutionary Algorithms -- Tree Adjoining Grammar -- Performance measures.
Contained By:	Springer Nature eBook
Subject:	Dynamics - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-90343-5
ISBN:	9783030903435

Automating data-driven modelling of dynamical systems = an evolutionary computation approach /
Khandelwal, Dhruv.

Automating data-driven modelling of dynamical systemsan evolutionary computation approach /[electronic resource] :by Dhruv Khandelwal. - Cham :Springer International Publishing :2022. - xxiii, 229 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral thesis accepted by Eindhoven University of Technology, Eindhoven, The Netherlands."

Introduction -- The State-of-the-art -- Preliminaries - Evolutionary Algorithms -- Tree Adjoining Grammar -- Performance measures.

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.

ISBN: 9783030903435

Standard No.: 10.1007/978-3-030-90343-5doiSubjects--Topical Terms:

713504
Dynamics
--Data processing.

LC Class. No.: QA845 / .K53 2022

Dewey Class. No.: 620.10540285

Automating data-driven modelling of dynamical systems = an evolutionary computation approach /
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245 1 0 $a Automating data-driven modelling of dynamical systems $h [electronic resource] : $b an evolutionary computation approach / $c by Dhruv Khandelwal.
260 $a Cham : $c 2022. $b Springer International Publishing : $b Imprint: Springer,
300 $a xxiii, 229 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5061
500 $a "Doctoral thesis accepted by Eindhoven University of Technology, Eindhoven, The Netherlands."
505 0 $a Introduction -- The State-of-the-art -- Preliminaries - Evolutionary Algorithms -- Tree Adjoining Grammar -- Performance measures.
520 $a 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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650 1 4 $a Control and Systems Theory. $3 3381515
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650 2 4 $a Applied Dynamical Systems. $3 3538870
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950 $a Intelligent Technologies and Robotics (SpringerNature-42732)