Self-Organizing Neural Networks for Signal Recognition

  • Jan Koutník
  • Miroslav Šnorek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


In this paper we introduce a self-organizing neural network that is capable of recognition of temporal signals. Conventional self-organizing neural networks like recurrent variant of Self-Organizing Map provide clustering of input sequences in space and time but the identification of the sequence itself requires supervised recognition process, when such network is used. In our network called TICALM the recognition is expressed by speed of convergence of the network while processing either learned or an unknown signal. TICALM network capabilities are shown on an experiment with handwriting recognition.


Input Signal Hide Markov Model Input Vector Signal Recognition Code Word 
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 2006

Authors and Affiliations

  • Jan Koutník
    • 1
  • Miroslav Šnorek
    • 1
  1. 1.Department of Computer Science and EngineeringCzech Technical UniversityPragueCzech Republic

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