Extraction of independent signal sources using a deflationary exploratory projection pursuit network
A nonlinear self-organising neural network is proposed, which employs hierarchic linear negative feedback, and this network is applied to the blind separation of independent source signals from their mixtures. Blind separation of sources has become an important area of research, with significant contributions recently being made from both the statistical signal processing and artificial neural network research communities. A nonlinear extension of a negative feedback network is developed and it is shown that hierarchic linear feedback provides a deflation of the network residuals, which are employed in the Hebbian learning of the network. As each of the output neuron weights converge to a separating vector, then the weighted feedback will remove the contribution of the extracted source from the remaining residual mixture. It is shown that the data driven self-organisation of the proposed network using only Hebbian and anti-Hebbian learning will extract the underlying source signals from the received mixture. The results of a simulation are reported, which demonstrates the ability of the network in restoring images after degradation with noise and interfering images.