Data-driven modelling of dynamical systems using tree adjoining grammar and genetic programming

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Abstract

State-of-the-art methods for data-driven modelling of non-linear dynamical systems typically involve interactions with an expert user. In order to partially automate the process of modelling physical systems from data, many EA-based approaches have been proposed for model-structure selection, with special focus on non-linear systems. Recently, an approach for data-driven modelling of non-linear dynamical systems using Genetic Programming (GP) was proposed. The novelty of the method was the modelling of noise and the use of Tree Adjoining Grammar to shape the search-space explored by GP. In this paper, we report results achieved by the proposed method on three case studies. Each of the case studies considered here is based on real physical systems. The case studies pose a variety of challenges. In particular, these challenges range over varying amounts of prior knowledge of the true system, amount of data available, the complexity of the dynamics of the system, and the nature of non-linearities in the system. Based on the results achieved for the case studies, we critically analyse the performance of the proposed method.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2673-2680
Number of pages8
ISBN (Electronic)978-1-7281-2153-6
DOIs
Publication statusPublished - 1 Jun 2019
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand
CityWellington
Period10/06/1913/06/19

Keywords

  • genetic programming
  • system identification
  • tree adjoining grammar

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