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Genetic Algorithm Assisted Wavelet Noise Reduction Scheme for Chaotic Signals

Author

Listed:
  • Xiao Hong Han

    (Taiyuan University of Technology)

  • Xiao Ming Chang

    (Taiyuan University of Technology)

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.

Suggested Citation

  • Xiao Hong Han & Xiao Ming Chang, 2011. "Genetic Algorithm Assisted Wavelet Noise Reduction Scheme for Chaotic Signals," Journal of Optimization Theory and Applications, Springer, vol. 151(3), pages 646-653, December.
    • Handle: RePEc:spr:joptap:v:151:y:2011:i:3:d:10.1007_s10957-011-9875-6
    DOI: 10.1007/s10957-011-9875-6
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    References listed on IDEAS

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    1. Aminghafari, Mina & Cheze, Nathalie & Poggi, Jean-Michel, 2006. "Multivariate denoising using wavelets and principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2381-2398, May.
    2. Wei, Guoliang & Shu, Huisheng, 2007. "H∞ filtering on nonlinear stochastic systems with delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(2), pages 663-670.
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