Knowledge incorporation and rule extraction in neural networks

Minoru Fukumi, Yasue Mitsukura, Norio Akamatsu

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

1 Citation (Scopus)


In this paper a new knowledge incorporation and rule extraction method in neural networks is presented. The rule form of an if–then type can be inserted into a neural network (NN) as knowledge of a problem. NN is then trained by using a set of training samples. In this case the structure learning algorithm with forgetting is used to generate a small-sized NN system. After the NN training, rules are extracted from it. The results of computer simulations show that this approach can generate obvious network architectures and as a result simple rules compared with conventional rule extraction methods.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2001 - International Conference, Proceedings
EditorsKurt Hornik, Georg Dorffner, Horst Bischof
PublisherSpringer Verlag
Number of pages6
ISBN (Print)3540424865, 9783540446682
Publication statusPublished - 2001
Externally publishedYes
EventInternational Conference on Artificial Neural Networks, ICANN 2001 - Vienna, Austria
Duration: 2001 Aug 212001 Aug 25

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


OtherInternational Conference on Artificial Neural Networks, ICANN 2001

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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