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1. Equivalence Between RAM-Based Neural Networks and Probabilistic Automata.

Author

de Souto, Marcilio C. P., Ludermir, Teresa B., and de Oliveira, Wilson R.

Subjects
*ARTIFICIAL neural networks, *RANDOM access memory, *PROBABILISTIC automata, *ALGORITHMS, *ALGEBRA, *ARTIFICIAL intelligence
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. [ABSTRACT FROM AUTHOR]

Published
2005
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