Data processing: artificial intelligence – Neural network – Learning method
Patent
1992-10-27
1999-07-27
Hafiz, Tariq R.
Data processing: artificial intelligence
Neural network
Learning method
706 16, G06F 1518
Patent
active
059307811
ABSTRACT:
A 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.
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Barhen Jacob
Toomarian Nikzad
Hafiz Tariq R.
Kusmiss John H.
The United States of America as represented by the Administrator
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