Blind signal flattening using warping neural modules

Author

Fiori, Simone ; Bucciarelli, Paolo ; Piazza, Francesco

Author_Institution

Dipt. di Elettronica e Autom., Ancona Univ., Italy

Volume

3

fYear

1998

fDate

4-9 May 1998

Firstpage

2312

Abstract

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

Keywords

Newton-Raphson method; entropy; neural nets; probability; random processes; signal processing; blind signal flattening; blind transformation algorithm; functional links; input-output mapping; nonlinear flexible parametric function; probability density function; random process; uniform hashing map; warping neural modules; Density functional theory; Entropy; Neurons; Probability density function; Process design; Random processes; Shape; Signal design; Signal processing; Speech processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.687222

Filename

687222