## Abstract

A new improved, easily implementible learning algorithm for blind separation of statistically independent unknown source signals is proposed. In contrast to the well known algorithms, two time trajectories of synaptic weights {w_{ij}(t)} and {w_{ij}(t)} a r e computed where w_{ij}(t) is the time average of w_{if}(t). Extensive computer simulation experiments have confirmed that the proposed learning algorithm assures a high convergence speed of the neural network for a blind identification problem, i.e. a quick recovering of unknown signals from the observation of a linear combination (mixture) of them. The algorithm can easily be extended to other applications.

Original language | English |
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Pages (from-to) | 1986-1987 |

Number of pages | 2 |

Journal | Electronics Letters |

Volume | 28 |

Issue number | 21 |

DOIs | |

Publication status | Published - Oct 1992 |

Externally published | Yes |

## Keywords

- Learning algorithms
- Neural networks
- Signal processing