Fuzzy decision neural networks and application to data fusion

J. S. Taur, S. Y. Kung

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

5 Scopus citations

Abstract

A decision-based neural network (DBNN) is extended to a fuzzy-decision neural network (FDNN), which is shown to offer classification/generalization performance improvements, especially when the data are not clearly separable. The hierarchical structure adopted make the computation process very efficient. The learning rule and some key properties of FDNN are described. A Bayesian paradigm offers an optimal approach to data fusion. This approach is explored. DBNN, together with a Bayesian approach, is proposed to formulate the data fusion process.

Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing III - Proceedings of the 1993 IEEE Workshop, NNSP 1993
EditorsC.A. Kamm, G.M. Kuhn, R. Chellappa, B. Yoon, S.Y. Kung
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages171-180
Number of pages10
ISBN (Electronic)0780309286, 9780780309289
DOIs
StatePublished - Jan 1 1993
Event1993 3rd IEEE-SP Workshop on Neural Networks for Signal Processing, NNSP 1993 - Linthicum Heights, United States
Duration: Sep 6 1993Sep 9 1993

Publication series

NameNeural Networks for Signal Processing III - Proceedings of the 1993 IEEE Workshop, NNSP 1993

Conference

Conference1993 3rd IEEE-SP Workshop on Neural Networks for Signal Processing, NNSP 1993
CountryUnited States
CityLinthicum Heights
Period9/6/939/9/93

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Signal Processing
  • Safety, Risk, Reliability and Quality

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