Equivalence between RAM-based neural networks and probabilistic automata

Authors:
M. C.P. de Souto;T. B. Ludermir;W. R. de Oliveira
Affiliations:
Dept. of Informatics & Appl. Math., Fed. Univ. of Rio Grande do Norte, Natal, Brazil;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

Citing 0
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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.