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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Łukasz Bartczuk, Piotr Dziwiński, Andrzej Cader

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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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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Metadaten
Titel
Symbolic Regression with the AMSTA+GP in a Non-linear Modelling of Dynamic Objects
verfasst von
Łukasz Bartczuk
Piotr Dziwiński
Andrzej Cader
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-91262-2_45