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CLC number: TN911.4

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Received: 2005-09-20

Revision Accepted: 2005-11-21

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.3 P.361~367

http://doi.org/10.1631/jzus.2006.A0361


Method and application of wavelet shrinkage denoising based on genetic algorithm


Author(s):  Ma Qi-ming, Wang Xuan-yin, Du Shuan-ping

Affiliation(s):  State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   maqiming1978@163.com

Key Words:  Wavelet transform, Translation-invariant wavelet transform, Genetic algorithm (GA), Correlation function


Ma Qi-ming, Wang Xuan-yin, Du Shuan-ping. Method and application of wavelet shrinkage denoising based on genetic algorithm[J]. Journal of Zhejiang University Science A, 2006, 7(3): 361~367.

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author="Ma Qi-ming, Wang Xuan-yin, Du Shuan-ping",
journal="Journal of Zhejiang University Science A",
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pages="361~367",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0361"
}

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%T Method and application of wavelet shrinkage denoising based on genetic algorithm
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%A Wang Xuan-yin
%A Du Shuan-ping
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%P 361~367
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%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0361

TY - JOUR
T1 - Method and application of wavelet shrinkage denoising based on genetic algorithm
A1 - Ma Qi-ming
A1 - Wang Xuan-yin
A1 - Du Shuan-ping
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 3
SP - 361
EP - 367
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Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0361


Abstract: 
genetic algorithm (GA) based on wavelet transform threshold shrinkage (WTS) and translation-invariant threshold shrinkage (TIS) is introduced into the method of noise reduction, where parameters used in WTS and TIS, such as wavelet function, decomposition levels, hard or soft threshold and threshold can be selected automatically. This paper ends by comparing two noise reduction methods on the basis of their denoising performances, computation time, etc. The effectiveness of these methods introduced in this paper is validated by the results of analysis of the simulated and real signals.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[8] Taswell, C., 2000. The what, how, and why of wavelet shrinkage denoising. IEEE Computational Science and Engineering, 3(2):12-19.

[9] Tu, K., Jiang, Y.Y., 2004. Development of Noise Reduction Algorithm for Underwater Signals. IEEE International Symposium on Underwater Technology, p.175-179.

[10] Xiao, M.H., Xue, J.Y., 2003. Research and application of genetic algorithm theory. Computer Engineering, 29(20):137-139 (in Chinese).

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[12] Yang, X.J., Zheng, J.L., 2003. Artificial Neural Network and Blind Signal Processing. Tsinghua Press, Beijing, p.301-324 (in Chinese).

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