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An Adaptive Training Method of Back-Propagation Algorithm
BibTeX
@MISC{Yoo_anadaptive,
author = {Jang-Hee Yoo and Jae-woo Kim and Jong-Uk Choi},
title = {An Adaptive Training Method of Back-Propagation Algorithm},
year = {}
}
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Abstract
Currently, the back-propagation is the most widely applied neural network algorithm at present. However, its slow learning speed and local minima problem are often cited as the major weakness of the algorithm. In this paper, described are an adaptive training algorithm based on selective retraining of patterns through error analysis, and dynamic adaptation of learning rate and momentum through oscillation detection for improving the performance of back-propagation algorithm. The usefulness of proposed algorithms was demonstrated in experiments with the XOR and Encode problems.
