Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Comparison of a neural network detector vs Neyman-Pearson optimal detector
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Neural networks for signal detection in non-Gaussian noise
IEEE Transactions on Signal Processing
MLPs for detecting radar targets in gaussian clutter
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Low complexity MLP-based radar detector: influence of the training algorithm and the MLP size
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Neural network detectors for composite hypothesis tests
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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This paper deals with the application of neural networks to approximate the Neyman-Pearson detector. The detection of Swerling I targets in white gaussian noise is considered. For this case, the optimum detector and the optimum decision boundaries are calculated. Results prove that the optimum detector is independent on TSNR, so, under good training conditions, neural network performance should be independent of it. We have demonstrated that the minimum number of hidden units required for enclosing the optimum decision boundaries is three. This result allows to evaluate the influence of the training algorithm. Results demonstrate that the LM algorithm is capable of finding excellent solutions for MLPs with only 4 hidden units, while the BP algorithm best results are obtained with 32 or more hidden units, and are worse than those obtained with the LM algorithm and 4 hidden units.