Comparison of a neural network detector vs Neyman-Pearson optimal detector

We optimize a neural network applied to binary detection such as those found in radar or sonar. Topics about designing the structure, training procedure and evaluating the performance, are discussed. The detector optimization is based on the use of a criterion function that yields a solution significantly superior to the typical sum-of-square-error. Using a modeled input, its performance is evaluated by Monte Carlo trials. As a result, detection curves are compared with the theoretical optimum ones (Neyman-Pearson detectors). For the model, and despite of the blind learning of the neural network, its performance is very close to optimal.