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Symbolic Regression with the AMSTA+GP in a Non-linear Modelling of Dynamic Objects

  • Łukasz BartczukEmail author
  • Piotr Dziwiński
  • Andrzej Cader
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

In this paper, we present a new version of the State Transition Algorithm, which allows to automatically determine the number and range of local models that describe the behaviour of a non-linear dynamic object. We used this data as input for genetic programming algorithm in order to create a simple functional model of the non-linear dynamic object which is not computationally demanded and has high accuracy.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Łukasz Bartczuk
    • 1
    Email author
  • Piotr Dziwiński
    • 1
  • Andrzej Cader
    • 2
    • 3
  1. 1.Institute of Computational IntelligenceCzęstochowa University of TechnologyCzęstochowaPoland
  2. 2.Information Technology InstituteUniversity of Social SciencesŁódźPoland
  3. 3.Clark UniversityWorchesterUSA

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