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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Overcoming the Local-Minimum Problem in Training Multilayer Perceptrons with the NRAE-MSE Training Method
verfasst von : James Ting-Ho Lo, Yichuan Gui, Yun Peng
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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The normalized risk-averting error (NRAE) training method presented in ISNN 2012 is capable of overcoming the local-minimum problem in training neural networks. However, the overall success rate is unsatisfactory. Motivated by this problem, a modification, called the NRAE-MSE training method is herein proposed. The new method trains neural networks with respect to NRAE with a fixed
λ
in the range of 10
6
-10
11
, and takes excursions to train with the standard mean squared error (MSE) from time to time. Once an excursion produces a satisfactory MSE with cross-validation, the entire NRAE-MSE training stops. Numerical experiments show that the NRAE-MSE training method has a success rate of 100% in all the testing examples each starting with a large number of randomly selected initial weights.