Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-28T16:16:07.572Z Has data issue: false hasContentIssue false
  • English
  • Français

The Placement of Electronic Circuits Problem: A Neural NetworkApproach

Published online by Cambridge University Press:  26 August 2010

A. Taik ,
M. Ettaouil ,
K. Elmoutaouakil  and
Y. Ghanou
Get access

Abstract

The goal of this paper is to apply the Continuous Hopfield Networks (CHN) to thePlacement of Electronic Circuit Problem (PECP). This assignment problem has been expressedas Quadratic Knapsack Problem (QKP). To solve the PECP via the CHN, we choose an energyfunction which ensures an appropriate balance between minimization of the cost functionand simultaneous satisfaction of the PECP constraints. In addition, the parameters of thisfunction must avoid some bad local minima. Finally, some computational experiments solvingthe PECP are included

Keywords

placement of the electronic circuits problem (PECP)continuous Hopfield networks (CHN)quadratic Knapsack problem (QKP)combinatorial problems
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 5 , Issue 7: JANO9 – The 9thInternational Conference on Numerical Analysis and Optimization , 2010 , pp. 109 - 115
Copyright
© EDP Sciences, 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

A. Cichocki, R. Unbehauen. Neural networks for optimization and signal processing. B.G. Teubner Stuttgart, 1993.
M. Ettaouil. Contribution à l’étude des problèmes de satisfaction de contraintes et à la programmation quadratiques en nombre entiers, allocation statiques de tâches dans les systèmes distrubués, thèse d’état, Université Sidi Mohammed ben Abdellah, F.S.T. de Fès, 1999.
Ettaouil, M., Ghanou, Y.. Neural architectures optimization and Genetic algorithms . Wesas Trasactions on Computer, 3 (2009), No. 8, 526-537.Google Scholar
Gee, A.H., Aiyer, S.V.B., Prager, R.W.. An analytical framework for optimizing neural networks . Neural Networks, (1993), No. 6, 79-97.CrossRefGoogle Scholar
Ghosh, A., Pal, S.K.. Object Background classification using Hopfield type neural networks . International Journal of Patten Recognition and Artificial Intelligence, (1992), No. 6, 989-1008.CrossRefGoogle Scholar
Hopfield, J.J., Tank, D.W.. Neural computation of decisions in optimisation problems . Biological Cybernetics, 52 (1985), 1-25.Google Scholar
Hopfield, J.J.. Neurons with graded response have collective computational properties like those of two-states neurons . Proceedings of the National Academy of Sciences of USA, 81 (1984), 3088-3092.CrossRefGoogle Scholar
M. Jünger, A. Martin, G. Reinel, R. Weismantel. Quadratic 0/1 optimization and a decomposition approach for the placement of electronic circuits. Report 91. 102, Institut für Informatik Universität köln, (1991).
Lee, B.W., Shen, B.J.. Hardware annealing in electronic neural networks . IEEE Trans, 1 (1990), 134 Google Scholar
N.M. Nasrabadi, C.Y. Choo. Hopfield network for stereo vision correspondence, New York: Marcel Dekker, 1994.
Talavàn, P.M., Yànez, J.. A continuous Hopfield network equilibrium points algorithm . Computers and Operations Research, 32 (2005), 2179-2196.CrossRefGoogle Scholar
Tatsumi, K., Yagi, Y., Tanino, T.. Improved projection Hopfield network for the quadratic assignment problem . SICE 2002, proceedings of the 41 st SICE annual conference, 4 (2002), 2295-2300.CrossRefGoogle Scholar