Automating Data-Driven Modelling of Dynamical Systems : An Evolutionary Computation Approach
Khandelwal, Dhruv
Automating Data-Driven Modelling of Dynamical Systems : An Evolutionary Computation Approach by Dhruv Khandelwal - First edition 2022 - 1 recurso en línea (XXIII, 229 páginas) 74 ilustraciones, 49 ilustraciones a color - Springer Theses Recognizing Outstanding Ph.D. Research 2190-5061 .
Introduction -- The State-of-the-art -- Preliminaries - Evolutionary Algorithms -- Tree Adjoining Grammar -- Performance measures.
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.
9783030903435
10.1007/978-3-030-90343-5 doi
Algoritmos
QA76.9.A43 / 2022 EB
Automating Data-Driven Modelling of Dynamical Systems : An Evolutionary Computation Approach by Dhruv Khandelwal - First edition 2022 - 1 recurso en línea (XXIII, 229 páginas) 74 ilustraciones, 49 ilustraciones a color - Springer Theses Recognizing Outstanding Ph.D. Research 2190-5061 .
Introduction -- The State-of-the-art -- Preliminaries - Evolutionary Algorithms -- Tree Adjoining Grammar -- Performance measures.
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.
9783030903435
10.1007/978-3-030-90343-5 doi
Algoritmos
QA76.9.A43 / 2022 EB