Abstract
All of the presented implementations of Artificial Neural Networks (A.N.N.) have been supposed to be working in ideal conditions, however, real applications will be subject to local and global perturbations. Since 1994, we have investigated the behaviour modelling of electronic A.N.N. with global perturbation conditions. We have scrutinised the behaviour analysis of a CMOS analogue implementation of synchronous Boltzmann Machine model with both ambient temperature and electrical perturbation. In this paper we present, using our model, the analysis of these global perturbations effects on learning capability of the above mentioned CMOS based analogue implementation. Simulation and experimental results have been exposed validating our concepts.
Preview
Unable to display preview. Download preview PDF.
References
C.A. Mead, “Analogue VLSI and neural systems”, Addison Wesley 1989.
R.F. Lyon and C.A. Mead, “An Analog electronic cochlea”, IEEE transactions on Acoustic, Speech and signal Processing, Vol. 36, PP 1119–1134, 1988.
L. Jackel, “Electronic neural networks”. In NATO ARW, Neuro-algorithms, architecture and applications, Les Arcs, 1989.
M. MAYOUBI, M. SCHAFER, S. SINSEL, “Dynamic Neural Units for Non-linear Dynamic Systems Identification”, From Natural to Artificial Neural Computation, LNCS Vol. 930, Springer Verlag, pp. 1045–1051,, 1995.
M. CHIABERGE, L. M. REYNERI, “Cintia: A Neuro-Fuzzy Real-Time Controller for Law-Power Embedded Systems”, IEEE Micro Vol. 15, pp. 40–47, June 1995.
G. MERCIER, K. MADANI, “CMAC Real-Time Adaptive Control Implementation on a DSP Based Card”,, From Natural to Artificial Neural Computation, LNCS, Vol. 930, Springer Verlag, pp. 1114–1120, 1995.
G. Bugmann, P. Sojka, M. Reiss, M. Plumbly, J. Taylor, “Direct Approaches to Improving the robustness of Multilayer Neural Networks”, Artificial Neural Networks 2, Elsevier science Pub, 1992.
J.J. Hopfield, “Neurons with graded response have collective computational properties like those of two state neurones”, Proceedings of the national Academy of science of U.S.A., vol 81 pp 3088–3092, 1984.
J.L. WYATT and D.L. STANDLEY, “Circuit design criteria for stable lateral inhibition neural networks “In IEEE International Symposium Circuits and systems, IEEE pp 997–1000, June 1988.
M.A. Sivilotti, M.R. EMERLING and C.A. Mead, “VLSI Architectures for implementation of Neural Network”. In AIP conference Proceedings on Neural Network for computing, J.S. DENKER, American Institute of physic, Snowbird, UTAH pp408–413, 1986.
M. VERLEYSEN and P. JESPERS, “precision of sum-of-product in Analog Neural Network”. In Proceedings of the first International workshop on Microelectronics for Neural Networks, Dortmund, RFA, June 1990.
K. MADANI, I. BERECHET, “Temperature Perturbation Effects on Image Processing Dedicated stochastic Artificial Neural Networks”, SPIE SYMPOSIUM ON ELECTRONIC IMAGING: Science and Technology, San Jose California-U.S.A., February 6–10 1994.
G.E. Hinton and T.J. Sejnowski, “learning in Boltzmann machines”. In Cognitive 85, PARIS, PP 283–290, 1985.
R. Azencott, “Synchronous Boltzmann Machines and their learning algorithms”. In NATO ARW, Springer-Verlag, les arcs, February 1989.
P. GARDA and E. BELHAIRE, “An Analog chip set with digital I/O for synchronous Boltzmann Machine.” “In VLSI for Artificial Intelligence and Neural Network Kluwer Academic, J.G. Delgado-frias and W.R. Moore, BOSTON, 1990.
V. LAFARGUE, “Contribution à la réalisation électronique de Réseaux de Neurones formels: Intégration mixte de l'apprentissage des machines de Boltzmann”; Ph. D. Report, thèse de doctorat en science de l'université PARIS XI, Orsay, January 1993.
K. MADANI, I. BERECHET, G. DE TREMIOLLES, “Analysis of limitations in Analog Implementation of stochastic Artificial Neural Network V, ORLANDO, FLORIDA, U.S.A., 4–8 pril 1994.
E. BELHAIR, “Contribution à la réalisation électronique de réseaux de Neurones Formels Intégration Analogique d'une machine de BOLTZMANN”; ph.D. report, thèse de doctorat en science de l'université Paris XI, Orsay February 1992.
Y.P. TSIVIDIS, “Operation and Modelling of the MOS Transistor”, Mc Graw Hill, 1988, PP148.
S.M. SZE “physics of Semiconductor Devices”. Wiley, 1981.
K. MADANI, G. DE TREMIOLLES, Perturbation Effects Analysis in Analogue Implementation of a stochastic Artificial Neural Network, SPIE International AeroSense'96 Symposium—Applications and Science of Artificial Neural Networks, Orlando, Florida, USA, 08–12 May 1996.
K. MADANI, G. DE TREMIOLLES, Global Perturbation Effects Analysis in a CMOS Analogue Implementation of Synchronous Boltzmann Machine, 3-rd International Workshop on Thermal Investigations of Integrated Circuits and Microstructures, IEEE-CNRS, Cannes—Côte d'Azur, September 21–23, 1997.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Madani, K., de Tremiolles, G. (1999). Effects of global perturbations on learning capability in a CMOS analogue implementation of synchronous Boltzmann machine. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100477
Download citation
DOI: https://doi.org/10.1007/BFb0100477
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66068-2
Online ISBN: 978-3-540-48772-2
eBook Packages: Springer Book Archive