Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison |
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Authors: | Varsta Markus Heikkonen Jukka Lampinen Jouko Millán José Del R |
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Affiliation: | (1) Laboratory of Computational Engineering, Helsinki University of Technology, Miestentie 3, P.O. Box 9400, FIN-02015 HUT, Finland;(2) Institute for Systems, Informatics and Safety, European Commission, Joint Research Centre, I-21020 Ispra (VA), Italy |
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Abstract: | This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments. |
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Keywords: | convergence analysis self-organizing maps temporal sequence processing |
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