UID:
almahu_9949255043402882
Format:
XXIII, 229 p. 74 illus., 49 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783030903435
Series Statement:
Springer Theses, Recognizing Outstanding Ph.D. Research,
Content:
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.
Note:
Introduction -- The State-of-the-art -- Preliminaries - Evolutionary Algorithms -- Tree Adjoining Grammar -- Performance measures.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030903428
Additional Edition:
Printed edition: ISBN 9783030903442
Additional Edition:
Printed edition: ISBN 9783030903459
Language:
English
DOI:
10.1007/978-3-030-90343-5
URL:
https://doi.org/10.1007/978-3-030-90343-5
URL:
Volltext
(URL des Erstveröffentlichers)
Bookmarklink
