Study of MLP and RBFN performance for signal detection
نویسندگان
چکیده
This paper deals with the application of Neural Networks to binary hypothesis tests based on multiple observations. The problem of detecting a desired signal in Additive-White-Gaussian-Noise is considered, assuming that the desired signal observations 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 not only are more robust when varying the TrainingSignal-to-Noise-Ratio and the number of hidden units, but the best approximation to the Neyman-Pearson detector is achieved with them.
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