## Abstract

We present a new learning algorithm for the blind separation of independent source signals having non-zero skewness (the 3rd-order cumulant) (the source signals have non-symmetric probability distribution.), from their linear mixtures. It is shown that for a class of source signals whose probability distribution functions is not symmetric, a simple adaptive learning algorithm using quadratic function (f(x) = x^{2}) is very efficient for blind source separation task. It is proved that all stable equilibria of the proposed learning algorithm are desirable solutions. Extensive computer simulation experiments confirmed the validity of the proposed algorithm.

Original language | English |
---|---|

Pages (from-to) | 61-68 |

Number of pages | 8 |

Journal | Neural Processing Letters |

Volume | 7 |

Issue number | 2 |

DOIs | |

Publication status | Published - 1998 |

Externally published | Yes |

## Keywords

- Blind source separation
- Higher-order statistics
- Neural networks
- Unsupervised learning