Abstract
Multifractal analysis is the well developed theory in the non-linear analysis of chaotic signals. Quantification of chaotic nature and complexity of the waveforms requires estimation of the Generalized Fractal Dimensions (GFD) spectrum where the complexity means higher variability in general fractal dimension spectrum. The focal theme of this paper is to develop a fuzzy multifractal theory to define the Fuzzy Generalized Fractal Dimensions (F-GFD) by introducing fuzzy membership function in classical Generalized Fractal Dimensions method. It was shown that, the designed Fuzzy GFD method accurately classifies the complexity of the chaotic waveforms such as Weierstrass functions by comparing graphically with the classical GFD method. Hence the fuzzy multifractal analysis performs significantly than the classical multifractal analysis and also the proposed Fuzzy GFD is a generalized method of the classical GFD.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barnsley M (1993) Fractals everywhere, 2nd edn. Academic, Boston
Easwaramoorthy D, Uthayakumar R (2010b) Analysis of biomedical EEG signals using wavelet transforms and multifractal analysis. In: Proceedings of the 1st IEEE international conference on communication control and computing technologies. IEEE Xplore Digital Library, pp 544–549
Easwaramoorthy D, Uthayakumar R (2010b) Analysis of EEG signals using advanced generalized fractal dimensions. In: Proceedings of the second international conference on computing, communication and networking technologies. IEEE Xplore Digital Library, pp 1–6
Easwaramoorthy D, Uthayakumar R (2010c) Estimating the complexity of biomedical signals by multifractal analysis. In: Proceedings of the IEEE students’ technology symposium. IEEE Xplore Digital Library, pp 6–11
Easwaramoorthy D, Uthayakumar R (2011) Improved generalized fractal dimensions in the discrimination between healthy and epileptic EEG signals. J Comput Sci 2(1):31–38
Falconer K (2003) Fractal geometry: mathematical foundations and applications, 2nd edn. Wiley, Chichester
Grassberger P (1983) Generalized dimensions of strange attractors. Phys Lett A 97:227–320
Hentschel H, Procaccia I (1983) The infinite number of generalized dimensions of fractals and strange attractors. Physica D 8:435–444
Lakshmanan M, Rajasekar S (2003) Nonlinear dynamics: integrability, chaos and patterns. Springer, Heidelberg
Mandelbrot B (1983) The fractal geometry of nature. W.H. Freeman and Company, New York
Renyi A (1955) On a new axiomatic theory of probability. Acta Math Hung 6:285–335
Shannon C (1998) The mathematical theory of communication. University of Illinois Press, Champaign
Uthayakumar R, Easwaramoorthy D (2012) Multifractal-wavelet based denoising in the classification of healthy and epileptic EEG signals. Fluct Noise Lett 11(4):1250034 (22 p.)
Acknowledgements
The research work has been supported by University Grants Commission (UGC – MRP and SAP), Government of India, New Delhi, India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Uthayakumar, R., Easwaramoorthy, D. (2014). Fuzzy Generalized Fractal Dimensions for Chaotic Waveforms. In: Banerjee, S., Erçetin, Ş. (eds) Chaos, Complexity and Leadership 2012. Springer Proceedings in Complexity. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7362-2_48
Download citation
DOI: https://doi.org/10.1007/978-94-007-7362-2_48
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7361-5
Online ISBN: 978-94-007-7362-2
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)