Discrete-time signal processing
Discrete-time signal processing
Self-organizing maps
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Independent component analysis for artefact separation in astrophysical images
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Automated labeling for unsupervised neural networks: a hierarchical approach
IEEE Transactions on Neural Networks
A high-performance VLSI architecture for the histogram peak-climbing data clustering algorithm
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Quasar selection from combined radio and optical surveys using neural networks
ADA'04 Proceedings of the 3rd international conference on Astronomical Data Analysis
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In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of a data reduction and data mining. The federation of heterogeneous large astronomical database which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).