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Proceedings Paper

Global perturbation effects on learning capability in a CMOS analog implementation of synchronous Boltzmann machine
Author(s): Kurosh Madani; Ghislain de Tremiolles
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Paper Abstract

A very large number of works concerning the area of Artificial Neural Networks (ANN) deal with implementation of these models, especially as digital or analogue CMOS integrated circuits. All of the presented implementations of A.N.N. have been supposed to be working in ideal conditions but real applications will be subject to global perturbations. Unfortunately, very few papers analyze the behavior of analogue implementation of neural network with such kind of perturbations. Since 1994, we have investigated the behavior modeling of electronic A.N.N. with global perturbation conditions. We have scrutinized the behavior analysis of a CMOS analogue implementation of synchronous Boltzmann Machine model with both ambient temperature and electrical perturbations (supply voltage) perturbation. In this paper we present, using our model, the analysis of these global perturbations effects on learning capability in a CMOS analogue implementation of synchronous Boltzmann Machine Simulation and experimental results have been exposed validating our concepts.

Paper Details

Date Published: 25 March 1998
PDF: 12 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304816
Show Author Affiliations
Kurosh Madani, Univ. Paris XII (France)
Ghislain de Tremiolles, Univ. Paris XII and IBM France (France)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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