A neuro-fuzzy architecture for high performance classification

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


The concept of combining modular neural networks has been recently exploited as a new direction for the development of highly reliable neural network systems in the area of pattern classification. In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. The experimental results with the recognition problem of on-fine handwriting characters show that the performance of individual networks could be improved significantly.

Original languageEnglish
Title of host publicationAdvances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers
EditorsTakeshi Furuhashi
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783540606079
Publication statusPublished - 1995
Event3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994 - Nagoya, Japan
Duration: 1994 Aug 91994 Aug 10

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


Other3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1995.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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