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Proceedings Paper

Pulsed recursive neural networks and resource allocation, part 1: static allocation
Author(s): Laurent Herault
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Paper Abstract

This paper presents a new recursive neural network to solve optimization problems. It is made of binary neutrons with feedbacks. From a random initial state, the dynamics alternate successively several pulsation and constraint satisfaction phases. Applying the previous neural network has the following advantages: (1) There is no need to precisely adjust some parameters in the motion equations to obtain good feasible solutions. (2) During each constraint satisfaction phase, the network converges to a feasible solution. (3) The convergence time in the constraint satisfaction phases is very fast: only a few updates of each neuron are necessary. (4) The end user can limit the global response time of the network which regularly provides feasible solutions. This paper describes such a neural network to solve a complex real time resource allocation problem and compare the performances to a simulated annealing algorithm.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205132
Show Author Affiliations
Laurent Herault, LETI/DSYS (France)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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