Earticle

초록

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.

목차

Abstract
 1. Introduction
 2. gpICA: Geometric Post-nonlinear ICA
 3. Scheme in 2D for gpICA Algorithm
 4. Improvement 1: Linearization with compensation
 5. Improvement 2: Linearization with Multiple Points
 6. Experiment Results: Mixture of Four Speech Signals
 7. Conclusions
 References

키워드

저자

• Erika Torres [ Faculty of Engineering, Distrital University FJC, Colombia ]
• Paula Ulloa [ Faculty of Engineering, Distrital University FJC, Colombia ]
• Andrés Gaona [ Faculty of Engineering, Distrital University FJC, Colombia ]
• Miguel Torres [ Faculty of Engineering, Javeriana University, Colombia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605