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Neural network training by integration of adjoint systems of equations forward in timeA method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically, it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved, but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. The trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies.
Document ID
19930008087
Acquisition Source
Legacy CDMS
Document Type
Other - Patent Application
Authors
Toomarian, Nikzad
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Barhen, Jacob
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
September 6, 2013
Publication Date
October 27, 1992
Subject Category
Cybernetics
Report/Patent Number
NAS 1.71:NPO-18586-1-CU
Accession Number
93N17276
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Patent
NASA-CASE-NPO-18586-1-CU
Patent Application
US-PATENT-APPL-SN-969868
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