Competitive learning for self organizing maps used in classification of partial discharges

Abstract

In this paper different competitive learning algorithms for self-organizing maps (SOM) are experimentally examined. The characterization of the results obtained is presented in terms of quality of SOM. The competitive learning algorithms evaluated through SOM are winner-takes-all, frequency sensitive competitive learning, and rival penalized competitive learning. Case study: their performance in the classification of partial discharges on power cables.

Keywords

• aprendizaje competitivo
• mapas autoorganizados
• descargas parciales
• métricas de calidad
• diagnóstico