Fuzzy-decision neural networks

J. S. Taur, S. Y. Kung

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

1 Scopus citations

Abstract

In a decision-based neural network(DBNN), the teacher only tells the correctness of the classification for each training pattern. In dealing with practical classification applications where significant overlap may exist between categories, a special care is needed to cope with the 'marginal' training patterns. For these situations, a soft decision is more appropriate. This motivates a fuzzy-decision neural network(FDNN) which incorporates a penalty criterion into the DBNNs. Following [2], a penalty function is proposed which treats the errors with equal penalty once the magnitude of error exceeds certain threshold. Theoretically, the FDNNs are less biased and they yield the minimum error rate when the number of the training patterns is very large. Simulation results confirm that the FDNN works more effectively than the DBNN when the training patterns are not separable.

Original languageEnglish (US)
Title of host publicationPlenary, Special, Audio, Underwater Acoustics, VLSI, Neural Networks
PublisherPubl by IEEE
PagesI-577-I-580
ISBN (Print)0780309464
StatePublished - Jan 1 1993
Event1993 IEEE International Conference on Acoustics, Speech and Signal Processing - Minneapolis, MN, USA
Duration: Apr 27 1993Apr 30 1993

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume1
ISSN (Print)0736-7791

Other

Other1993 IEEE International Conference on Acoustics, Speech and Signal Processing
CityMinneapolis, MN, USA
Period4/27/934/30/93

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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