TY - GEN
T1 - Blind source separation for noisy time series by combining non-Gaussianity and time-correlation
AU - Zhang, Hongjuan
AU - Guo, Chonghui
AU - Shi, Zhenwei
PY - 2008
Y1 - 2008
N2 - This paper addressed the separation of noisy time series (noisy signals with time structure). Based on the non-Gaussianity of innovations, we first present an objective function with negentropy forms about innovations of time series. Furthermore, this criterion is extended for the noisy time series separation through combing Gaussian moments into it. Maximizing this objective function, a simple blind source separation algorithm is presented. Validity and performance of the described approach are demonstrated by computer simulations.
AB - This paper addressed the separation of noisy time series (noisy signals with time structure). Based on the non-Gaussianity of innovations, we first present an objective function with negentropy forms about innovations of time series. Furthermore, this criterion is extended for the noisy time series separation through combing Gaussian moments into it. Maximizing this objective function, a simple blind source separation algorithm is presented. Validity and performance of the described approach are demonstrated by computer simulations.
UR - https://www.scopus.com/pages/publications/57649131021
U2 - 10.1109/ICNC.2008.477
DO - 10.1109/ICNC.2008.477
M3 - 会议稿件
AN - SCOPUS:57649131021
SN - 9780769533049
T3 - Proceedings - 4th International Conference on Natural Computation, ICNC 2008
SP - 121
EP - 125
BT - Proceedings - 4th International Conference on Natural Computation, ICNC 2008
T2 - 4th International Conference on Natural Computation, ICNC 2008
Y2 - 18 October 2008 through 20 October 2008
ER -