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Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic Pattern Recognition Neural Network

  • Dragos Calitoiu
  • B. John Oommen
  • Dorin Nusbaumm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

The usual goal of modeling natural and artificial perception involves determining how a system can extract the object that it perceives from an image which is noisy. The “inverse” of this problem is one of modeling how even a clear image can be perceived to be blurred in certain contexts. We propose a chaotic model of Pattern Recognition (PR) for the theory of “blurring”. The paper, which is an extension to a Companion paper [3] demonstrates how one can model blurring from the view point of a chaotic PR system. Unlike the Companion paper in which the chaotic PR system extracts the pattern from the input, this paper shows that the perception can be “blurred” if the dynamics of the chaotic system are modified. We thus propose a formal model, the Mb-AdNN, and present a rigorous analysis using the Routh-Hurwitz criterion and Lyapunov exponents. We also demonstrate, experimentally, the validity of our model by using a numeral dataset.

Keywords

Lyapunov Exponent Chaotic System Companion Paper Associative Memory Transient Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dragos Calitoiu
    • 1
  • B. John Oommen
    • 1
  • Dorin Nusbaumm
    • 1
  1. 1.Carleton UniversityOttawaCanada

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