Evaluating the effectiveness of the fine-tuned learning enhancement to backpropagation

Steven P. Coy, Bruce L. Golden, Edward A. Wasil, George Runger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Fine-tuned learning is an enhancement to the standard backpropagation algorithm that relies on the notion of data approximation followed by sequential data refinement. It appears to be a quick and accurate variant of standard backpropagation. In this paper, we conduct extensive computational testing of fine-tuned learning in order to evaluate its effectiveness in reducing processing time and avoiding early convergence to local minima. Our results show that fine-tuned learning improves the performance of standard backpropagation.

Original languageEnglish (US)
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
Place of PublicationFairfield, NJ, United States
PublisherASME
Pages105-111
Number of pages7
Volume7
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 Artificial Neural Networks in Engineering Conference, ANNIE'97 - St.Louis, MO, USA
Duration: Nov 9 1997Nov 12 1997

Other

OtherProceedings of the 1997 Artificial Neural Networks in Engineering Conference, ANNIE'97
CitySt.Louis, MO, USA
Period11/9/9711/12/97

ASJC Scopus subject areas

  • Software

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    Coy, S. P., Golden, B. L., Wasil, E. A., & Runger, G. (1997). Evaluating the effectiveness of the fine-tuned learning enhancement to backpropagation. In Intelligent Engineering Systems Through Artificial Neural Networks (Vol. 7, pp. 105-111). ASME.