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An Importance Sampling Approach to the Estimation of Algorithm Performance in Automated Algorithm Design Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Towards a Universal Modeller of Chaotic Systems

Author : Erik Berglund

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

This paper proposes a Machine Learning (ML) algorithm and examines its dynamic properties when trained on chaotic time series. It will be demonstrated that the output of the ML system is itself more chaotic if it is trained on a chaotic input than if it is trained on non-chaotic input. The proposed ML system builds on to the Parameter-Less Self-Organising Map 2 (PLSOM2) and introduces new developments.

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previous chapter Comparing Two Approaches for Solving Constrained Global Optimization Problems
next chapter An Approach for Generating Test Problems of Constrained Global Optimization
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Metadata
Title
Towards a Universal Modeller of Chaotic Systems
Author
Erik Berglund
Copyright Year
2017
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-319-69403-0

Electronic ISBN: 978-3-319-69404-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69404-7_23

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