Automating Data-Driven Modelling of Dynamical Systems

Dhruv Khandelwal

ISBN 10: 3030903451 / ISBN 13: 9783030903459

Published by Springer International Publishing, Springer International Publishing Feb 2023, 2023

About this Item

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.

Seller Inventory # 9783030903459

Bibliographic Details

• Title: Automating Data-Driven Modelling of Dynamical Systems
• Publisher: Springer International Publishing, Springer International Publishing Feb 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 3030903451
• ISBN 13: 9783030903459
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.