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A Systematic Chaotic Noise Reduction Method Combining with Neural Network

  • Min Han
  • Yuhua Liu
  • Jianhui Xi
  • Zhiwei Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3497)

Abstract

It has been found that noise limits the prediction of deterministic chaotic system. Due to the lack of knowledge on dynamical system and nature of noise, the estimate of noise level is obviously important to the commonly used noise reduction method. On the basis of noise level estimate and optimized method, a systematic chaotic noise reduction method is proposed combining with Finite Impulse Response Neural Network (FIRNN) in this paper. Firstly, the initial noise level is estimated using wavelet analysis. Then, a Local Projection noise reduction method is applied while a FIRNN is used as a main diagnostic tool to determine the optimal noise level. Simulation on real monthly noisy sunspot time series shows that the proposed method works properly for noisy chaotic signals.

Keywords

Root Mean Square Error Noise Level Noise Reduction Noise Reduction Method Noise Level Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Min Han
    • 1
  • Yuhua Liu
    • 1
  • Jianhui Xi
    • 1
    • 2
  • Zhiwei Shi
    • 1
  1. 1.School of Electronic and Information EngineeringDalian University of TechnologyDalianChina
  2. 2.Department of AutomationShenyang Institute of Aeronautical EngineeringShenyangChina

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