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Published in:
The Complex Geometry of the Mandelbrot Set Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Evolutionary Systems in Complex Signal Analysis

Author : Tomas Brandejsky

Published in: ISCS 2013: Interdisciplinary Symposium on Complex Systems

Publisher: Springer Berlin Heidelberg

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Abstract

All complex systems presenting chaotic behaviour are non-linear ones and many problems of their analysis and modelling are caused by application of linear or pseudo-linear models which are not able to represent all aspects of signals generated by these systems. Experiments with some natural-based signal data like e.g. EEG ones concluded presence of typical composite periodic functions, like sin(sin(x)). These functions have specific behaviours which will be presented. Especially, they are non-stationar, have continuous spectrum and thus it is hard to apply usual tools like Fourier transform. To analyse these signals, evolutionary GPA-ES system was used.

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previous chapter Emergent Phenomena in Natural Complex Systems
next chapter Macroscopic Description of Complex Self-Organizing System: Belousov–Zhabotinsky Reaction
Literature
1.
Brandejsky, T.: The use of local models optimized by genetic programming algorithm in biomedical-signal analysis. In: Handbook of optimization From Classical to Modern Approach, pp. 697–716. Springer, Heidelberg (2012). ISBN 978-3-642-30503-0
2.
Brandejsky, T.: Multi-layered evolutionary system suitable to symbolic model regression. In: Recent Researches in Applied Informatics, vol. 1, pp. 222–225. WSEAS Press, Athens (2011)
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Faber, J., Pekny, J., Pieknik, R., et al.: Simultaneous recording of electric and metabolic brain activity. Neural Netw. World 20(4), 539–557 (2010)
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Bouchner, P., Faber, J., Novotny, S., Tichy, T.: Driver‘s attention level improvement with use of biofeedback stimulation incorporated into driving simulator. Neural Netw. World 19(1), 109–118 (2009). ISSN 1210–0552
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Koza, J.R., Bennett III, F.H., Andre, D., Keane, M.A.: Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann, San Francisco (1999)
6.
Brandejsky, T.: Parallel implementations of GPA-ES algorithm. In: Mendel 2012. Brno: VUT in Brno, Faculty of Mechanical Engineering, pp. 30–34 (2012). ISBN 978-80-214-4540-6
7.
Brandejsky, T.: Small populations in GPA-ES algorithm. In: Mendel 2013. Brno: VUT in Brno, Faculty of Mechanical Engineering, pp. 31–36 (2013). ISBN 978-80-214-4755-4
8.
Brandejsky, T.: Influence of operator set to chaotic system symbolic regression. In: Mendel 2013. Brno: VUT in Brno, Faculty of Mechanical Engineering, pp. 63–68 (2013). ISBN 978-80-214-4755-4
Metadata
Title
Evolutionary Systems in Complex Signal Analysis
Author
Tomas Brandejsky
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
ISCS 2013: Interdisciplinary Symposium on Complex Systems

Print ISBN: 978-3-642-45437-0

Electronic ISBN: 978-3-642-45438-7

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-642-45438-7_10

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