Experiments with Neural Networks for Real Time Implementation of Control
Abstract
This paper describes a neural network based controller for allocating capacity in a telecommunications network. This system was proposed in order to overcome a "real time" response constraint. Two basic architectures are evaluated: 1) a feedforward network-heuristic and; 2) a feedforward network-recurrent network. These architectures are compared against a linear programming (LP) optimiser as a benchmark. This LP optimiser was also used as a teacher to label the data samples for the feedforward neural network training algorithm. It is found that the systems are able to provide a traffic throughput of 99% and 95%, respectively, of the throughput obtained by the linear programming solution. Once trained, the neural network based solutions are found in a fraction of the time required by the LP optimiser.
Cite
Text
Campbell et al. "Experiments with Neural Networks for Real Time Implementation of Control." Neural Information Processing Systems, 1995.Markdown
[Campbell et al. "Experiments with Neural Networks for Real Time Implementation of Control." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/campbell1995neurips-experiments/)BibTeX
@inproceedings{campbell1995neurips-experiments,
title = {{Experiments with Neural Networks for Real Time Implementation of Control}},
author = {Campbell, Peter K. and Dale, Michael and Ferrá, Herman L. and Kowalczyk, Adam},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {973-979},
url = {https://mlanthology.org/neurips/1995/campbell1995neurips-experiments/}
}