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Genetic Estimation of Iterated Function Systems for Accurate Fractal Modeling in Pattern Recognition Tools

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10404))

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Abstract

In this paper, we describe an algorithm to estimate the parameters of Iterated Function System (IFS) fractal models. We use IFS to model Speech and Electroencephalographic signals and compare the results. The IFS parameters estimation is performed by means of a genetic optimization approach. We show that the estimation algorithm has a very good convergence to the global minimum. This can be successfully exploited by pattern recognition tools. However, the set-up of the genetic algorithm should be properly tuned. In this paper, besides the optimal set-up description, we describe also the best tradeoff between performance and computational complexity. To simplify the optimization problem some constraints are introduced. A comparison with suboptimal algorithms is reported. The performance of IFS modeling of the considered signals are in accordance with known measures of the fractal dimension.

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Author information

Authors and Affiliations

  1. DIA Department, University of Trieste and ICAR-CNR, Trieste, Italy

    Alfredo Cuzzocrea

  2. DIA Department, University of Trieste, Trieste, Italy

    Enzo Mumolo

  3. CSECS Department, University of Messina, Messina, Italy

    Giorgio Mario Grasso

Authors
  1. Alfredo Cuzzocrea
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  2. Enzo Mumolo
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  3. Giorgio Mario Grasso
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Cuzzocrea, A., Mumolo, E., Grasso, G.M. (2017). Genetic Estimation of Iterated Function Systems for Accurate Fractal Modeling in Pattern Recognition Tools. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-62392-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62391-7

  • Online ISBN: 978-3-319-62392-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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