Hybrid Techniques for Improving ICA Algorithms

J.M. Górriz (Spain)


Independent Component Analysis, Genetic Algorithms, FastICA, MatLab


In this paper we present a novel method for blindly sepa rating unobservable independent component signals from their linear mixtures, using genetic algorithms (GA) to minimize the nonconvex and nonlinear cost functions and a design for a tool for the blind separation of signals (BSS) using the Matlab visual environment. ICA has been pro posed to resolve the problem of BSS, while GAs com prise an efficient artificial intelligence technique for the op timization of diverse problems. The algorithm proposed combines these two approaches, its efficiency being essen tial for applications presenting both a high degree of dimen sionality and time restrictions (i.e. real time applications such as occur in biomedicine and in finance).

Important Links:

Go Back