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 |
|---|---|
| Pages | 283-292 |
| Number of pages | 10 |
| Publication status | Published - 1999 Dec 1 |
| 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) |
|---|---|
| City | Madison, WI, USA |
| Period | 99/8/23 → 99/8/25 |
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering