Overview
- Presents a novel approach for automating system identification
- Offers novel solutions to multi-criteria system identification problems
- Reviews fundamental concepts of system identification
Part of the book series: Springer Theses (Springer Theses)
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About this book
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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Keywords
- Automated System Identification
- Multi-criteria model selection
- Model Selection Problem
- User-specified Performance Measures
- Parametric Model Representation
- Multi-objective Optimization Problem
- Bi-level Optimization
- Grammar-based Model Representation
- Grammar-based Identification
- Evolutionary algorithms in System Identification
- Symbolic Regression for Dynamical Systems
- Memetic Algorithm
- Non-linear Model Selection
Table of contents (8 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Automating Data-Driven Modelling of Dynamical Systems
Book Subtitle: An Evolutionary Computation Approach
Authors: Dhruv Khandelwal
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-030-90343-5
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-90342-8Published: 04 February 2022
Softcover ISBN: 978-3-030-90345-9Published: 04 February 2023
eBook ISBN: 978-3-030-90343-5Published: 03 February 2022
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XXIII, 229
Number of Illustrations: 25 b/w illustrations, 49 illustrations in colour
Topics: Control and Systems Theory, Computational Intelligence, Complexity