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Modular, Hierarchical Learning By Artificial Neural NetworksModular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.
Document ID
19960018798
Acquisition Source
Legacy CDMS
Document Type
Other - NASA Tech Brief
Authors
Baldi, Pierre F.
(Caltech)
Toomarian, Nikzad
(Caltech)
Date Acquired
August 17, 2013
Publication Date
March 1, 1996
Publication Information
Publication: NASA Tech Briefs
Volume: 20
Issue: 3
ISSN: 0145-319X
Subject Category
Electronic Systems
Report/Patent Number
NPO-19077
Accession Number
96B10103
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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