Equivalence between RAM-based neural networks and probabilistic automata.
IEEE Trans Neural Netw
; 16(4): 996-9, 2005 Jul.
Article
en En
| MEDLINE
| ID: mdl-16121742
ABSTRACT
In this letter, the computational power of a class of random access memory (RAM)-based neural networks, called general single-layer sequential weightless neural networks (GSSWNNs), is analyzed. The theoretical results presented, besides helping the understanding of the temporal behavior of these networks, could also provide useful insights for the developing of new learning algorithms.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Reconocimiento de Normas Patrones Automatizadas
/
Modelos Estadísticos
/
Redes Neurales de la Computación
Tipo de estudio:
Evaluation_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Neural Netw
Asunto de la revista:
INFORMATICA MEDICA
Año:
2005
Tipo del documento:
Article