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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	Februar 2023
Genre:	Naturwissensch., Medizin, Technik
	AutomatedSystemIdentification / Bi-levelOptimization / EvolutionaryalgorithmsinSystemIdentification / Grammar-basedIdentification / Grammar-basedModelRepresentation / Künstliche Intelligenz (KI) / Mathematik für Ingenieure / MemeticAlgorithm / ModelSelectionProblem / Multi-criteriamodelselection / multi-objectiveoptimizationproblem / Non-linearModelSelection / ParametricModelRepresentation / Regelungstechnik / SymbolicRegressionforDynamicalSystems / User-specifiedPerformanceMeasures mehr...
ISBN:	9783030903459
EAN-Code:	9783030903459
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 15 mm
Gewicht:	394 gr
Seiten:	256
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.