### Abstract

In this paper, we propose a novel Independent Component Analysis (ICA) algorithm which enables to separate mixtures of sub-Gaussian, super-Gaussian and Gaussian primary source signals. Alternative activation functions in the algorithm are derived by using parameterized t-distribution and generalized Gaussian distribution density models. The functions are self-adaptive based on estimating the high-order moments of extracted signals. Moreover, a stability condition of the proposed algorithm for separating the true solution is given. Simulation experiment results are presented to illustrate the effectiveness and performance of the proposed algorithm.

Original language | English |
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Title of host publication | Neural Networks for Signal Processing - Proceedings of the IEEE Workshop |

Place of Publication | Piscataway, NJ, United States |

Publisher | IEEE |

Pages | 283-292 |

Number of pages | 10 |

Publication status | Published - 1999 |

Externally published | Yes |

Event | Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA Duration: 1999 Aug 23 → 1999 Aug 25 |

### Other

Other | Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) |
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City | Madison, WI, USA |

Period | 99/8/23 → 99/8/25 |

### Fingerprint

### ASJC Scopus subject areas

- Signal Processing
- Software
- Electrical and Electronic Engineering

### Cite this

*Neural Networks for Signal Processing - Proceedings of the IEEE Workshop*(pp. 283-292). IEEE.