On the Capability of Neural Networks to Approximate the Neyman-Pearson Detector - A Theoretical Study

P. Jarabo-Amores, R. Gil-Pita, M. Rosa-Zurera, F. López-Ferreras

Abstract

In this paper, the application of neural networks for approximating the Neyman-Pearson detector is considered. We propose a strategy to identify the training parameters that can be controlled for reducing the effect of approximation errors over the performance of the neural network based detector. The function approximated by a neural network trained using the mean squared-error criterion is deduced, without imposing any restriction on the prior probabilities of the clases and on the desired outputs selected for training, proving that these parameters play an important role in controlling the sensibility of the neural network detector performance to approximation errors. Another important parameter is the signal-to-noise ratio selected for training. The proposed strategy allows to determine its best value, when the statistical properties of the feature vectors are known. As an example, the detection of gaussian signals in gaussian interference is considered.

References

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Paper Citation


in Harvard Style

Jarabo-Amores P., Gil-Pita R., Rosa-Zurera M. and López-Ferreras F. (2004). On the Capability of Neural Networks to Approximate the Neyman-Pearson Detector - A Theoretical Study . In Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004) ISBN 972-8865-14-7, pages 67-74. DOI: 10.5220/0001150100670074


in Bibtex Style

@conference{anns04,
author={P. Jarabo-Amores and R. Gil-Pita and M. Rosa-Zurera and F. López-Ferreras},
title={On the Capability of Neural Networks to Approximate the Neyman-Pearson Detector - A Theoretical Study},
booktitle={Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004)},
year={2004},
pages={67-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001150100670074},
isbn={972-8865-14-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004)
TI - On the Capability of Neural Networks to Approximate the Neyman-Pearson Detector - A Theoretical Study
SN - 972-8865-14-7
AU - Jarabo-Amores P.
AU - Gil-Pita R.
AU - Rosa-Zurera M.
AU - López-Ferreras F.
PY - 2004
SP - 67
EP - 74
DO - 10.5220/0001150100670074