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Neuromorphic Learning From Noisy DataTwo reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.
Document ID
19930000306
Acquisition Source
Legacy CDMS
Document Type
Other - NASA Tech Brief
Authors
Merrill, Walter C.
(NASA Lewis Research Center, Cleveland, OH.)
Troudet, Terry
(Sverdrup Technology, Inc.)
Date Acquired
August 16, 2013
Publication Date
May 1, 1993
Publication Information
Publication: NASA Tech Briefs
Volume: 17
Issue: 5
ISSN: 0145-319X
Subject Category
Mathematics And Information Sciences
Report/Patent Number
LEW-15304
Accession Number
93B10306
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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