Processing of incomplete fuzzy data using artificial neural networks

Marek J. Patyra, Taek M Kwon

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

1 Scopus citations

Abstract

In this paper, a degenerated fuzzy-number processing system based on artificial neural networks (ANN) is introduced. The digital representation of fuzzy numbers is assumed, where the universe of discourse is discretized into n equally divided intervals. The representation of the membership function values is transformed into binary quantized values which have its maximum at 2m - 1 where m is the number of data bits used in the system. It is proposed that fuzzy number processing be performed in two basic stages. The first stage performs the retrieval of fuzzy data consisting of degenerated fuzzy numbers, and the second stage performs the desired fuzzy operations on the retrieved data. The method of incomplete fuzzy-number retrieval is proposed based on an ANN structure which is trained to estimate the missing membership function values.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Fuzzy Systems
PublisherPubl by IEEE
Pages429-433
Number of pages5
ISBN (Print)0780306155
StatePublished - Jan 1 1993
EventSecond IEEE International Conference on Fuzzy Systems - San Francisco, CA, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Fuzzy Systems

Other

OtherSecond IEEE International Conference on Fuzzy Systems
CitySan Francisco, CA, USA
Period3/28/934/1/93

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