Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

Authors:
Markus Varsta;Jukka Heikkonen;Jouko Lampinen;José Del R. Millán
Affiliations:
Laboratory of Computational Engineering, Helsinki University of Technology, Miestentie 3, P.O. Box 9400, FIN-02015 HUT, Finland. E-mail: markus.varsta@hut.fi;Laboratory of Computational Engineering, Helsinki University of Technology, Miestentie 3, P.O. Box 9400, FIN-02015 HUT, Finland. E-mail: jukka.heikkonen@hut.fi;Laboratory of Computational Engineering, Helsinki University of Technology, Miestentie 3, P.O. Box 9400, FIN-02015 HUT, Finland. E-mail: jouko.lampinen@hut.fi;European Commission, Joint Research Centre, Institute for Systems, Informatics and Safety, I-21020 Ispra (VA), Italy. E-mail: jose.millan@jrc.it
Venue:
Neural Processing Letters
Year:
2001

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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.