Abstract
The effect of the coefficients used in the conventional back propagation algorithm on training connectionist models is discussed, using a vowel recognition task in speech processing as an example. Some weaknesses of the use of fixed coefficients are described and an adaptive algorithm using variable coefficients is presented. This is found to be efficient and robust in comparison with the fixed parameter case, to give fast near optimal training and to avoid trial and error choice of fixed coefficients. It has also been successfully used in a vision processing application.
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