DocumentCode
3220053
Title
Unified formulation for training recurrent networks with derivative adaptive critics
Author
Feldkamp, L.A. ; Puskorius, G.V. ; Prokhorov, D.V.
Author_Institution
Ford Res. Lab., Dearborn, MI, USA
Volume
4
fYear
1997
fDate
9-12 Jun 1997
Firstpage
2268
Abstract
We present a procedure for obtaining the derivatives used in training a recurrent network that combines in a unified framework the techniques of backpropagation through time and derivative adaptive critics. The resulting formulation is consistent with previous descriptions, but has the advantage of allowing the mentioned techniques to be used together in a proportion that is appropriate to a given problem
Keywords
backpropagation; mathematical programming; recurrent neural nets; backpropagation through time; derivative adaptive critics; dual heuristic programming; learning; recurrent neural networks; Adaptive systems; Area measurement; Backpropagation; Computational intelligence; Dynamic programming; Equations; Laboratories; Learning; Neurodynamics; Programmable control;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks,1997., International Conference on
Conference_Location
Houston, TX
Print_ISBN
0-7803-4122-8
Type
conf
DOI
10.1109/ICNN.1997.614397
Filename
614397
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