Context learning with the self-organizing map
In this paper a recurrent self-organizing map (RSOM) algorithm is proposed for temporal sequence processing. The RSOM algorithm is close in nature to the Kohonen's self-organizing map, except that in the RSOM context of the temporal sequence is involved both in the best matching unit finding and in the adaptation of the weight vectors of the map via an introduced recursive difference equation associated for each unit of the map. The experimental results in the paper demonstrate that the RSOM is able to learn and distinguish temporal sequences, and that the RSOM algorithm can be utilized, for instance, in electroencephalogram (EEG) based epileptic activity detection.
Bibliographic Reference: Paper presented: International Workshop on Self-Organizing Maps, Helsinki (FI), June 4-6, 1997
Availability: Available from (1) as Paper EN 40644 ORA
Record Number: 199711095 / Last updated on: 1997-09-16
Original language: en
Available languages: en