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Artificial Neural Network

  • Sun-Chong Wang
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 743)

Abstract

Inspired by the sophisticated functionality of human brains where hundreds of billions of interconnected neurons process information in parallel, researchers have successfully tried demonstrating certain levels of intelligence on silicon. Examples include language translation and pattern recognition software. While simulation of human consciousness and emotion is still in the realm of science fiction, we, in this chapter, consider artificial neural networks as universal function approximators. Especially, we introduce neural networks which are suited for time series forecasts.

Keywords

Neural Network Artificial Neural Network Hide Layer Input Vector Hide Neuron 
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. An introduction to self-organizing map is, T. Kohonen, “Self-organizing Maps”, 2nd ed. Springer-Verlag, Berlin (1997)zbMATHCrossRefGoogle Scholar
  2. Two textbooks on neural networks are, C.M. Bishop, “Neural Networks for Pattern Recognition”, Oxford University Press, Oxford (1995)Google Scholar
  3. B.D.Ripley, “Pattern Recognition and Neural Networks”, Cambridge University Press, Cambridge (1996)zbMATHGoogle Scholar
  4. Numerous resources on neural networks can be found in the on-line FAQ located at, ftp://ftp.sas.com/pub/neural/FAQ.htmlGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Sun-Chong Wang
    • 1
  1. 1.TRIUMFCanada

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