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Wavelet Neural Networks Approach for Dynamic Measuring Error Decomposition

  • Yan Shen
  • Bing Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Combining the time-frequency location and multiple-scale analysis of wavelet transform with the nonlinear mapping and self-learning of neural networks, an error decomposition method in dynamic measuring system is proposed. According to whole-system dynamic accuracy theory, the whole-error model of dynamic measuring system is given, and then the whole-error is decomposed into sub-errors by wavelet neural networks, which are traced so that units of the system are found which generate these errors and error transmission characteristic is controlled.

Keywords

Wavelet Packet Wavelet Neural Network Error Trace Error Decomposition Decomposed Signal 
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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References

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    Gaouda, A.M., Salama, M.A.: Power Quality Detection and Classification Using Wavelet-Multi-Resolution Signal Decomposition. IEEE Trans. power delivery. 14(4), 1469–1476 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yan Shen
    • 1
  • Bing Guo
    • 2
    • 3
  1. 1.University of Electronic Science and Technology of ChinaChengduChina
  2. 2.Kyungwon UniversitySeongnam, Gyeonggi-DoSouth Korea
  3. 3.Sichuan UniversityChengduChina

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