Unified formulation for training recurrent networks with derivative adaptive critics

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...

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Bibliographic Details
Published inProceedings of International Conference on Neural Networks (ICNN'97) Vol. 4; pp. 2268 - 2272 vol.4
Main Authors Feldkamp, L.A., Puskorius, G.V., Prokhorov, D.V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Adaptive systems
Area measurement
Backpropagation
Computational intelligence
Dynamic programming
Equations
Laboratories
Learning
Neurodynamics
Programmable control
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Summary: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.
ISBN:0780341228
9780780341227
DOI:10.1109/ICNN.1997.614397