NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methodsGeneral formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.
Document ID
19890041663
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Leong, Harrison Monfook
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1988
Subject Category
Cybernetics
Meeting Information
Meeting: IEEE Conference on Neural Information Processing Systems
Location: Denver, CO
Country: United States
Start Date: November 8, 1987
End Date: November 12, 1987
Accession Number
89A29034
Funding Number(s)
CONTRACT_GRANT: NCC2-408
Distribution Limits
Public
Copyright
Other

Available Downloads

There are no available downloads for this record.
No Preview Available