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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.
| Autorius: | Dhruv Khandelwal |
| Serija: | Springer Theses |
| Leidėjas: | Springer International Publishing |
| Išleidimo metai: | 2023 |
| Knygos puslapių skaičius: | 256 |
| ISBN-10: | 3030903451 |
| ISBN-13: | 9783030903459 |
| Formatas: | 235 x 155 x 15 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach“
