# An introduction to artificial neural networks

### C.A.L. Bailer-Jones, R. Gupta, H.P. Singh

Artificial neural networks are algorithms which have been developed to
tackle a range of computational problems. These range from modelling
brain function to making predictions of time-dependent phenomena to
solving hard (NP-complete) problems. In this introduction we describe
a single, yet very important, type of network known as a feedforward
network. This network is a mathematical model which can be trained to
learn an arbitrarily complex relationship between a data and a
parameter domain, so can be used to solve interpolation and
classification problems. We discuss the structure, training and
interpretation of these networks, and their implementation, taking the
classification of stellar spectra as an example.

in Automated Data Analysis in Astronomy,

R. Gupta, H.P. Singh, C.A.L. Bailer-Jones (eds.), Narosa Publishing House, New Delhi,
India, 2001

[PDF version] 859Kb, 18 pages

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Coryn Bailer-Jones, calj at mpia-hd.mpg.de

Last modified: 12 February 2001