Performance of Synthetic Neural Network Classification of Noisy Radar Signals
Abstract
This study evaluates the performance of the multilayer-perceptron and the frequency-sensitive competitive learning network in iden(cid:173) tifying five commercial aircraft from radar backscatter measure(cid:173) ments. The performance of the neural network classifiers is com(cid:173) pared with that of the nearest-neighbor and maximum-likelihood classifiers. Our results indicate that for this problem, the neural network classifiers are relatively insensitive to changes in the net(cid:173) work topology, and to the noise level in the training data. While, for this problem, the traditional algorithms outperform these sim(cid:173) ple neural classifiers, we feel that neural networks show the poten(cid:173) tial for improved performance.
Cite
Text
Ahalt et al. "Performance of Synthetic Neural Network Classification of Noisy Radar Signals." Neural Information Processing Systems, 1988.Markdown
[Ahalt et al. "Performance of Synthetic Neural Network Classification of Noisy Radar Signals." Neural Information Processing Systems, 1988.](https://mlanthology.org/neurips/1988/ahalt1988neurips-performance/)BibTeX
@inproceedings{ahalt1988neurips-performance,
title = {{Performance of Synthetic Neural Network Classification of Noisy Radar Signals}},
author = {Ahalt, Stanley C. and Garber, F. D. and Jouny, I. and Krishnamurthy, Ashok K.},
booktitle = {Neural Information Processing Systems},
year = {1988},
pages = {281-288},
url = {https://mlanthology.org/neurips/1988/ahalt1988neurips-performance/}
}