Automating Data-Driven Modelling of Dynamical Systems
Springer International Publishing (Verlag)
978-3-030-90342-8 (ISBN)
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.
Introduction.- The State-of-the-art.- Preliminaries - Evolutionary Algorithms.- Tree Adjoining Grammar.- Performance measures.
Erscheinungsdatum | 05.02.2022 |
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Reihe/Serie | Springer Theses |
Zusatzinfo | XXIII, 229 p. 74 illus., 49 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 541 g |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Technik ► Elektrotechnik / Energietechnik | |
Schlagworte | Automated System Identification • Bi-level Optimization • Evolutionary algorithms in System Identification • Grammar-based Identification • Grammar-based Model Representation • memetic algorithm • Model Selection Problem • Multi-criteria model selection • multi-objective optimization problem • Non-linear Model Selection • Parametric Model Representation • Symbolic Regression for Dynamical Systems • User-specified Performance Measures |
ISBN-10 | 3-030-90342-7 / 3030903427 |
ISBN-13 | 978-3-030-90342-8 / 9783030903428 |
Zustand | Neuware |
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