Neural detectors with variable threshold

In this paper we present and compare a selected set of neural network architectures used to detect a known signal corrupted by Gaussian and non-Gaussian noise components. We propose and simulate a new structure for the neural detector that uses a variable threshold in the decision stage. The practical implementation and the robustness of this neural detector are also considered