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Empirical validation of the performance of a class of transient detector

  • Philip J. Jacob
  • Andrew D. Ball
Evolutionary Machine Learning and Classifier Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)

Abstract

Transient detection in the presence of noise is a problem which occurs in many areas of engineering. A description is given of a classifier system suitable for the identification of high frequency waveforms. It uses the Wavelet Transform for signal pre-processing to produce a more parsimonious representation of the signal to be identified. A comparison is presented of the use of a Forward Selection algorithm and a Genetic Algorithm to pick appropriate indicator variables as inputs to a classifier. A Radial Basis Function neural network is employed to model the class conditional probability density function. The classifier is applied to the identification of a number of high frequency Acoustic Emission signals, which are difficult to classify,.

Keywords

Acoustic Emission Wavelet Transform Wavelet Coefficient Acoustic Emission Signal Radial Basis Function Neural Network 
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

  1. 1.
    Bishop, C. Neural Networks for Pattern Recognition, Oxford University Press, 1995.Google Scholar
  2. 2.
    Broomhead, D.S., Lowe, D., “Multi-variable Functional Interpretation and Adaptive Networks”, Complex Systems, Vol. 2, 1988, pp.321–355.Google Scholar
  3. 3.
    Chui, Ch., K., An Introduction to Wavelets. Wavelet Analysis and its Applications, Vol. 1, Academic Press, Boston, 1992.Google Scholar
  4. 4.
    Daubechies, I., “The Wavelet Transform, Time-frequency Localization and Signal Analysis”, I.EEE. Trans. Information Theory, Vol. 36, 1990, pp.961–1005.Google Scholar
  5. 5.
    Kittler, J., “Feature Selection and Extraction”, Handbook of Pattern Recognition and Image Processing, Eds.Young, T.Y., Fu, K.S., Academic Press, 1986, pp. 60–81.Google Scholar
  6. 6.
    Newland, D.E., An Introduction to Random Vibrations, Spectral and Wavelet Analysis, 3rd Ed., Longman Scientific and Technical, Essex, 1994.Google Scholar
  7. 7.
    Pudil, P., Novovicová, J., Ferri, F., “New Tools for Knowledge Guided Approach to Statistical Pattern Recognition”, AISB96 Workshop, 1 April, 1996, Brighton, UK.Google Scholar
  8. 8.
    Press, W.H., et al., Numerical Recipes in C, Cambridge University Press, 1994, pp. 808–812.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Philip J. Jacob
    • 1
  • Andrew D. Ball
    • 1
  1. 1.School of EngineeringUniversity of ManchesterManchesterUK

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