Evolutionary time–frequency distributions using Bayesian regularised neural network model

Simulation results show that regularisation, with input training under Mackay's evidence framework, produces results that are highly concentrated along the instantaneous frequencies of the individual components present in the test TFDs.

Abstract

Time–frequency distributions (TFDs) that are highly concentrated in the time–frequency plane are computed using a Bayesian regularised neural network model. The degree of regularisation is automatically controlled in the Bayesian inference framework and produces networks with better generalised performance and lower susceptibility to over-fitting. Spectrograms and Wigner transforms of various known signals form the training set. Simulation results show that regularisation, with input training under Mackay's evidence framework, produces results that are highly concentrated along the instantaneous frequencies of the individual components present in the test TFDs. Various parameters are compared to establish the effectiveness of the approach.