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A modified error function for the backpropagation algorithm
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DOI：10.1016/j.neucom.2003.12.006.png)
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Abstract
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We have noted that the local minima problem in the backpropagation algorithm is usually caused by update disharmony between weights connected to the hidden layer and the output layer. To solve this problem, we propose a modified error function. It can harmonize the update of weights connected to the hidden layer and those connected to the output layer by adding one term to the conventional error function. It can thus avoid the local minima problem caused by such disharmony. Simulations on a benchmark problem and a real classification task have been performed to test the validity of the modified error function. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
backpropagation
learning
local minima
modified error function
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