NDLI: An on-line learning algorithm for recurrent neural networks using variational methods

Content Provider	IEEE Xplore Digital Library
Author	Won-Geun Oh Byung-Shul Suh
Copyright Year	1997
Description	Author affiliation: Dept. of Comput. & Commun. Eng., Sunchon Nat. Univ., Chonnam, South Korea (Won-Geun Oh)
Abstract	In this paper, an on-line learning algorithm for recurrent neural networks (RNN) using optimal control and a variational method is proposed. First, we obtain optimal weights given by a two-point boundary-value problem using the variational methods. And then the local gradient descent algorithm is derived such that on-line training is possible. This method is intended to be used on learning complex dynamic mappings between time-varying input-output data. Therefore it is useful for nonlinear control, identification, and signal processing applications of RNN. Simulation results for nonlinear plant identification are illustrated.
Starting Page	659
Ending Page	662
File Size	339808
Page Count	4
File Format	PDF
ISBN	0780336941
DOI	10.1109/MWSCAS.1997.666225
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-08-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Signal processing algorithms Optimal control Neural networks Nonlinear dynamical systems History Signal mapping Iterative algorithms Robustness Control systems
Content Type	Text
Resource Type	Article

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An on-line learning algorithm for recurrent neural networks using variational methods