Building Adaptive Basis Functions with a Continuous Self-OrganizingMap

Authors:
Marcos M. Campos;Gail A. Carpenter
Affiliations:
Boston University, Center for Adaptive Systems and Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 02215, U.S.A. gail@cns.bu.edu;Boston University, Center for Adaptive Systems and Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 02215, U.S.A. gail@cns.bu.edu
Venue:
Neural Processing Letters
Year:
2000

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Abstract

This paper introduces CSOM, a continuous version of the Self-Organizing Map(SOM). The CSOM network generates maps similar to those created with theoriginal SOM algorithm but, due to the continuous nature of the mapping,CSOM outperforms the SOM on function approximation tasks. CSOM integratesself-organization and smooth prediction into a single process. This is adeparture from previous work that required two training phases, one toself-organize a map using the SOM algorithm, and another to learn a smoothapproximation of a function. System performance is illustrated with threeexamples.