NDLI: Optimal training parameters in multilayer feedforward networks

Content Provider	IEEE Xplore Digital Library
Author	Wendemuth, A. Gerke, M.
Copyright Year	1999
Description	Author affiliation: Univ. of Hagen, Germany (Wendemuth, A.)
Abstract	We present a systematic investigation of the training behavior for multilayer feedforward neural networks. Usually learning is governed by three metaparameters, which are learning rate, momentum and offset. We apply a (nearly) exhaustive search method to find optimal parameter sets throughout the complete sequence of training cycles, regarding the training process as a finite state network in the space of metaparameter configurations. Minimization of training time is achieved by methods of dynamic programming. A detailed analysis is given for the choice of error criteria and necessary widths and prunings of network 'beams' in search space. It is shown for a representative set of training patterns, that the number of network training iterations is largely independent of both, the metaparameter initialization and the random weight initialization. Training is twice as fast as with conventional metaparameter adaptation strategies, such as RPROP or local fuzzy inference.
Starting Page	1881
Ending Page	1884
File Size	429217
Page Count	4
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.832667
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent networks Nonhomogeneous media Multi-layer neural network Neural networks Feedforward neural networks Dynamic programming Search methods Minimization methods Transfer functions Backpropagation
Content Type	Text
Resource Type	Article

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