Automating data-driven modelling of dynamical systems : an evolutionary computation approach / Dhruv Khandelwal.
2022
QA845 .K53 2022
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Details
Title
Automating data-driven modelling of dynamical systems : an evolutionary computation approach / Dhruv Khandelwal.
Author
ISBN
9783030903435 (electronic bk.)
3030903435 (electronic bk.)
9783030903428
3030903427
3030903435 (electronic bk.)
9783030903428
3030903427
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (some color).
Item Number
10.1007/978-3-030-90343-5 doi
Call Number
QA845 .K53 2022
Dewey Decimal Classification
620.1/0540285
Summary
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 users 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.
Note
"Doctoral thesis accepted by Eindhoven University of Technology, Eindhoven, The Netherlands."
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Includes bibliographical references.
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text file PDF
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Description based on print version record.
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Springer theses.
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Table of Contents
Introduction
The State-of-the-art
Preliminaries - Evolutionary Algorithms
Tree Adjoining Grammar
Performance measures.
The State-of-the-art
Preliminaries - Evolutionary Algorithms
Tree Adjoining Grammar
Performance measures.