Equivalence between RAM-based neural networks and probabilistic automata

Author

De Souto, Marcilio C P ; Ludermir, Teresa B. ; De Oliveira, Wilson R.

Author_Institution

Dept. of Informatics & Appl. Math., Fed. Univ. of Rio Grande do Norte, Natal, Brazil

Volume

16

Issue

4

fYear

2005

fDate

7/1/2005 12:00:00 AM

Firstpage

996

Lastpage

999

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.

Keywords

computability; learning automata; neural nets; probabilistic automata; random-access storage; learning algorithm; probabilistic automata; random access memory based neural network; single layer sequential weightless neural network; temporal behavior; Computer networks; Informatics; Learning automata; Mathematics; Neural networks; Neurons; Physics; Random access memory; Table lookup; Transfer functions; Automata theory; RAM-based neural networks; computability; probabilistic automata; weightless neural networks (WNNs); Algorithms; Computer Simulation; Models, Statistical; Neural Networks (Computer); Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2005.849838

Filename

1461443