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Nonlinear Approaches to Automatic Elicitation of Distributed Oscillatory Clusters in Adaptive Self-organized System

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Distributed Computing and Artificial Intelligence

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 151))

Abstract

Chaotic neural networks find more and more applications in pattern recognition systems. However hybrid multidisciplinary solutions that combine advances from physics and artificial intelligence fields tend to enrich the complexity of designed systems and add more discussion points. This paper questions the applicability of well known chaotic time-series metrics (Shannon entropy, Kolmogorov entropy, Fractal dimension, Gumenyuk metric, complete and lag synchronization estimations) to simplify elicitation of distributed oscillatory clusters that store clustering results of a problem. Computer modeling results gives evidence that in case of clustering simple datasets the metrics are rather effective; however the concept of averaging out agent’s dynamics fails when the clusters in the input dataset are linearly non-separable.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, St. Petersburg State Polytechnical University, Russia, 194021, St. Petersburg, Politechnicheskaya 21

    Elena N. Benderskaya

  2. Graduate School of Management, St. Petersburg State University, Russia, 199004, St. Petersburg, Volkhovsky Per. 3

    Sofya V. Zhukova

Authors
  1. Elena N. Benderskaya
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  2. Sofya V. Zhukova
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Corresponding author

Correspondence to Elena N. Benderskaya .

Editor information

Editors and Affiliations

  1. Dept. Computer Science &, Intelligent Systems, Osaka Prefecture University, Gakuen-chu 1-1, Sakai, Osaka, 599-8531, Japan

    Sigeru Omatu

  2. , Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan F. De Paz Santana

  3. , Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Sara Rodríguez González

  4. Escuela Politécnica Superior (EPS), Depto. Informática, Universidad Carlos III de Madrid, Avenida de la Universidad Carloss III 22, Madrid, 28270, Spain

    Jose M. Molina

  5. , Data Processing and Simulation Group, Universidad Politécnica de Madrid, Calle de Ramiro de Maeztu, 7, Madrid, 28040, Spain

    Ana M. Bernardos

  6. Faculty of Science, Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan M. Corchado Rodríguez

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© 2012 Springer-Verlag Berlin Heidelberg

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Benderskaya, E.N., Zhukova, S.V. (2012). Nonlinear Approaches to Automatic Elicitation of Distributed Oscillatory Clusters in Adaptive Self-organized System. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_88

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  • DOI: https://doi.org/10.1007/978-3-642-28765-7_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28764-0

  • Online ISBN: 978-3-642-28765-7

  • eBook Packages: EngineeringEngineering (R0)

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