Self-Organizing Neural Networks

The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years.



This volume presents the theory and applications of self organizing neural network models which perform the independent component analysis (ICA) transformation and blind source separation (BSS). It covers the fundamental concepts of information theory, higher order statistics and in formation geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas: noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualization, data mining and biomedical data analysis.