earticle

영어

This paper presents two modifications of the gpICA (geometric post non-linear independent component analysis) algorithm. gpICA algorithm is a novel method to solve the PNL (Post non-linear) scheme. We propose these modifications to improve the mean squared error, the correlation of the recovered signals and algorithm reliability . The first improvement, called compensation, takes advantage of the implicit information given by the point to be linearized. On the other hand, while the original gpICA algorithm uses two sets of two points to make an update, our second modification uses two sets of four points. We present experimental results which validates the effectiveness of each modification. The PNL applications can be seen in sensor array processing, digital satellite, microwave communications, biological systems and nonlinear blind source separation tasks. gpICA can recover the original sources of a nonlinear mixture, unlike some of the other nonlinear BSS algorithms, it does not require any assumption on the distribution of the input signals.