Abstract
Signal classification performance using both multilayer neural network (MLNN) and conventional signal processing methods are theoretically compared under a limited observation period and computational load. Signals with N samples are classified based on the frequency components. A comparison is carried out based on the degree of freedom of the signal detection regions in an N-dimensional signal space. As a result, the MLNN has a higher degree of freedom, and can provide a more flexible performance for classifying the signals than the conventional methods. This analysis is further investigated through computer simulations. Multi-frequency signals and a real application in dial tone receiver, are considered. As a result, the MLNN can provide a much higher accuracy than the conventional signal processing methods.
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