摘要
We report the results of using the fast independent component analysis （FastICA） algorithm to realize Mind extraction of chaotic signals. Two cases are taken into consideration： namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail It is also shown that regarding a noise as an independent source is not always correct.
We report the results of using the fast independent component analysis （FastICA） algorithm to realize Mind extraction of chaotic signals. Two cases are taken into consideration： namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail It is also shown that regarding a noise as an independent source is not always correct.
基金
Supported by the National Natural Science Foundation of China under Grant No 60572025, the Foundation of the Education Ministry of China under Grant No NCET-04-0813, the Natural Science Foundation of Guangdong Province under Grant Nos 04205783 and 07006496, the Doctorate Foundation of South China University of Technology, and the Key Subject Fund of Shanghai City under Grant No T0102.
