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.
|出版ステータス||Published - 1999 12 1|
|イベント||Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA|
継続期間: 1999 8 23 → 1999 8 25
|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