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Genetic Algorithm Assisted Wavelet Noise Reduction Scheme for Chaotic Signals

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Abstract

We present a Genetic Algorithm-based wavelet denoising method which incorporates a Genetic Algorithm within a wavelet framework for threshold optimization. The new method not only intelligently adapts itself to different types of noise without any prior knowledge of the noise, but also balances the preservation of dynamics against the degree of noise reduction by optimizing the Signal-to-Noise Ratio and the Liu’s error factor. The presented method performs better than the state-of-the-art wavelet-based denoising methods when applied to chaotic signals.

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Authors and Affiliations

  1. College of Computer Science and Technology & College of Software, Taiyuan University of Technology, No. 79 Yingze West Street, Taiyuan, 030024, Shanxi, P.R. China

    Xiao Hong Han & Xiao Ming Chang

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  1. Xiao Hong Han
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  2. Xiao Ming Chang
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Correspondence to Xiao Ming Chang.

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Han, X.H., Chang, X.M. Genetic Algorithm Assisted Wavelet Noise Reduction Scheme for Chaotic Signals. J Optim Theory Appl 151, 646–653 (2011). https://doi.org/10.1007/s10957-011-9875-6

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  • DOI: https://doi.org/10.1007/s10957-011-9875-6

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