Automating Data-Driven Modelling of Dynamical Systems / Springer Theses (PDF)
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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.
- Autor: Dhruv Khandelwal
- 2022, 1st ed. 2022, 229 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3030903435
- ISBN-13: 9783030903435
- Erscheinungsdatum: 03.02.2022
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- Grösse: 10 MB
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