Blind signal flattening using warping neural modules
Simone Fiori, Paolo Bucciarelli, Francesco Piazza · 2002
The aim of the paper is to present a new blind transformation algorithm which makes flat (uniform) the probability density function of a random process. The same algorithm allows us to find a uniform hashing map between a set of source symbols and a set of associated ones. As a transformation a non-linear flexible parametric function is used. Its parameters are continuously changed through time for maximizing the entropy of the transformed random process. In a neural context, such a function will represent the input-output mapping performed by a single neuron endowed with functional links.