Udvidet returret til d. 31. januar 2025

Automating Data-Driven Modelling of Dynamical Systems - Dhruv Khandelwal - Bog

Bag om Automating Data-Driven Modelling of Dynamical Systems

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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783030903428
  • Indbinding:
  • Hardback
  • Sideantal:
  • 229
  • Udgivet:
  • 4. februar 2022
  • Udgave:
  • 12022
  • Størrelse:
  • 155x235x0 mm.
  • Vægt:
  • 553 g.
  • 8-11 hverdage.
  • 11. december 2024
Forlænget returret til d. 31. januar 2025

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Beskrivelse af Automating Data-Driven Modelling of Dynamical Systems

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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