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Geophysical phenomena classification by artificial neural networksSpace science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.
Document ID
19960022594
Acquisition Source
Headquarters
Document Type
Conference Paper
Authors
Gough, M. P.
(Sussex Univ. Brighton, United Kingdom)
Bruckner, J. R.
(Sussex Univ. Brighton, United Kingdom)
Date Acquired
August 17, 2013
Publication Date
January 1, 1995
Publication Information
Publication: Visualization techniques in space and atmospheric sciences
Subject Category
Documentation And Information Science
Accession Number
96N25538
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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