Parallel processing by implication-based neuro-fuzzy systems

Danuta Rutkowska, Robert Nowicki, Yoichi Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


The system composed of various implication-based neurofuzzy networks in one parallel structure is proposed in this paper. Different phases of data processing are distinguished, i.e. learning, testing, and problem solving. A competetive learning of the neuro-fuzzy networks is employed. This learning method refers to the first layer, which is the same in every network. The system with fuzzy parameters of membership functions is also considered. In this case, the neuro-fuzzy architectures may be viewed as fuzzy inference neural networks with fuzzy parameters, and treated analogously to fuzzy neural networks.

Original languageEnglish
Title of host publicationParallel Processing and Applied Mathematics - 4th International Conference, PPAM 2001, Revised Papers
EditorsJerzy Wasniewski, Roman Wyrzykowski, Jack Dongarra, Marcin Paprzycki
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783540437925
Publication statusPublished - 1 Jan 2002
Event4th International Conference on Parallel Processing and Applied Mathematics, PPAM 2001 - Naleczow, Poland
Duration: 9 Sep 200112 Sep 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Parallel Processing and Applied Mathematics, PPAM 2001


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