Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach

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@Book{Khandelwal:book,

• author = "Dhruv Khandelwal",
• title = "Automating Data-Driven Modelling of Dynamical Systems: An Evolutionary Computation Approach",
• publisher = "Springer",
• year = "2022",
• series = "Springer Theses",
• month = "4 " # feb,
• keywords = "genetic algorithms, genetic programming, 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",
• isbn13 = "978-3030903428",
• ISSN = "2190-5053",
• URL = "https://www.amazon.co.uk/Automating-Data-Driven-Modelling-Dynamical-Systems/dp/3030903427",
• DOI = "doi:10.1007/978-3-030-90343-5",
• size = "252 pages",
• abstract = "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 non-linear 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.",
• notes = "Part of: Springer Theses",

}

Genetic Programming entries for Dhruv Khandelwal

Citations