论文信息 - Blind Separation of Mixing Chaotic Signals Based on ICA Using Kurtosis

Blind Separation of Mixing Chaotic Signals Based on ICA Using Kurtosis

There are some methods that separate the mixing chaotic signals, but they have to use the internal properties of signals and special constraints. By exploiting the independence of sources in the mixing chaotic signals, the fixed-point ICA based on the kurtosis to separate the mixtures is used. It is accordance with the ICA (Independent Component Analysis) estimation principle of maximum nongaussianity. The results by computer simulation indicate that the mixed chaotic signals, by using the method, can be separated and get the sources signals fast and effectively.

Fang Yi | Yang Jianning

[1] Xiang Li,et al. Uncertain chaotic behaviours of chaotic-based frequency- and phase-modulated signals , 2011 .

[2] Danilo P. Mandic,et al. Fast Independent Component Analysis Algorithm for Quaternion Valued Signals , 2011, IEEE Transactions on Neural Networks.

[3] Walter Vogler,et al. Signal transition graph decomposition: internal communication for speed independent circuit implementation , 2011, IET Comput. Digit. Tech..

[4] Frequency and phase synchronization in neuromagnetic cortical responses to flickering-color stimuli , 2009, 0910.5030.

[5] Sladana Spasic,et al. Independent complexity patterns in single neuron activity induced by static magnetic field , 2011, Comput. Methods Programs Biomed..

[6] Michael G. Sideris,et al. On the capabilities of the multi-channel singular spectrum method for extracting the main periodic and non-periodic variability from weekly GRACE data , 2012 .

[7] Jon Atli Benediktsson,et al. Hyperspectral Image Classification With Independent Component Discriminant Analysis , 2011, IEEE Transactions on Geoscience and Remote Sensing.