Pulsed recursive neural networks and resource allocation, part 1: static allocation

Laurent Herault

doi:10.1117/12.205132

6 April 1995 Pulsed recursive neural networks and resource allocation, part 1: static allocation

Laurent Herault

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205132
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Laurent Herault "Pulsed recursive neural networks and resource allocation, part 1: static allocation", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205132

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