This paper introduces the fixed-point learning algorithm based on independent component analysis (ICA); the model and process of this algorithm and simulation results are presented. Kurtosis was adopted as the estimation rule of independence. The results of the experiment show that compared with the traditional ICA algorithm based on random grads, this algorithm has advantages such as fast convergence and no necessity for any dynamic parameter, etc. The algorithm is a highly efficient and reliable method in blind signal separation.
LI Hongyan, MA Jianfen, LI Deng'ao, WANG Huakui
. Fixed-point blind source separation algorithm
based on ICA[J]. Frontiers of Electrical and Electronic Engineering, 2008
, 3(3)
: 343
-346
.
DOI: 10.1007/s11460-008-0064-9
1. Hyvärinen A Oja E Independent component analysis:algorithms and applicationsNeural Networks 2000 13(4–5)411430. doi:10.1016/S0893‐6080(00)00026‐5
2. Hyvärinen A Fastand robust fixed-point algorithm for independent component analysisIEEE Transactions on Neural Networks 1999 10(3)626634. doi:10.1109/72.761722
3. Shi Z Tang H Tang Y A new fixed-point algorithm for independent component analysisNeural Computing 2004 56467473
4. Jutten C Herault J Blind Separation of sources,part 1: an adaptive algorithm based on neuromimetic architectureSignal Processing 1991 24(1)110. doi:10.1016/0165‐1684(91)90079‐X
5. Karhunen J Oja E Wang L et al.A class of neural networks for independent componentanalysisIEEE Transactions on Neural Networks 1997 8(3)486504. doi:10.1109/72.572090
