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Neural network for processing both spatial and temporal data with time based back-propagationNeural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.
Document ID
19940015893
Acquisition Source
Legacy CDMS
Document Type
Other - Patent
Authors
Villarreal, James A. (NASA Lyndon B. Johnson Space Center Houston, TX, United States)
Shelton, Robert O. (NASA Lyndon B. Johnson Space Center Houston, TX, United States)
Date Acquired
August 16, 2013
Publication Date
October 12, 1993
Subject Category
Cybernetics
Accession Number
94N20366
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Patent
US-PATENT-5,253,329|NASA-CASE-MSC-21874-1
Patent Application
US-PATENT-APPL-SN-813556

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