An introduction to artificial neural networks
By Coryn A. L. Bailer-Jones, Ranjan Gupta, Harinder P. Singh. 2001
826 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/b763114076b5ee7429e15c0aea0668f1d433931f
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- Paulo Eduardo Maciel de Almeida
- Magali Rezende Gouvêa Meireles
- Marcelo Simoes
- Mariagrazia Dotoli
- Luc A. Andriantiatsaholiniaina
Citations in the corpus
- A comprehensive review for industrial applicability of artificial neural networks (2003, background): “The momentum coefficients are, usually, set according to a schedule similar to the one for the learning coefficients [128].”