Información del artículo
Resumen
En este artículo, los autores presentan un nuevo enfoque de síntesis para entrenar memorias asociativas implementadas con redes neuronales recurrentes. Los pesos de la red recurrente se determinan como la soluci ón óptima de la combinación lineal de vectores soporte. El algoritmo de entrenamiento propuesto maximiza el margen entre los patrones de entrenamiento y la superficie de decisi ón. El problema de diseño considera: 1) la obtención de los pesos por medio del algoritmo de hiperplano óptimo utilizado para máquinas de vector soporte y 2) la obtención de las condiciones para reducir el número total de memorias espurias. El nuevo algoritmo desarrollado se utiliza para diseñar una memoria asociativa que diagnostique fallas en centrales termoeléctricas.
Palabras clave:
Memoria asociativa
red neuronal recurrente
máquinas de vector soporte
hiperplano óptimo
detección y diagnóstico de fallas
central termoeléctrica
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