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.
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References
An introduction to self-organizing map is, T. Kohonen, “Self-organizing Maps”, 2nd ed. Springer-Verlag, Berlin (1997)
Two textbooks on neural networks are, C.M. Bishop, “Neural Networks for Pattern Recognition”, Oxford University Press, Oxford (1995)
B.D.Ripley, “Pattern Recognition and Neural Networks”, Cambridge University Press, Cambridge (1996)
Numerous resources on neural networks can be found in the on-line FAQ located at, ftp://ftp.sas.com/pub/neural/FAQ.html
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© 2003 Springer Science+Business Media New York
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Wang, SC. (2003). Artificial Neural Network. In: Interdisciplinary Computing in Java Programming. The Springer International Series in Engineering and Computer Science, vol 743. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0377-4_5
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DOI: https://doi.org/10.1007/978-1-4615-0377-4_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5046-0
Online ISBN: 978-1-4615-0377-4
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