Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Three learning phases for radial-basis-function networks
Neural Networks
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
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 binary detection based on multiple observations. The problem of detecting a desired signal in Additive-White-Gaussian-Noise is considered, assuming that the desired signal samples are also gaussian, independent and identically distributed random variables. The test statistic is then the squared magnitude of the observation vector and the optimum boundary is a hyper-sphere in the input space. The dependence of the neural network detector on the Training-Signal-to-Noise-Ratio and the number of hidden units is studied. Results show that Radial Basis Function Networks are less dependent on the Training-Signal-to-Noise-Ratio and the number of hidden units than Multilayer Perceptrons, and approximate better the Neyman-Pearson detector.