Process identification through modular neural networks and rule extraction (extended abstract)

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

    21 Downloads (Pure)


    Monolithic neural networks may be trained from measured data to establish knowledge about the process. Unfortunately, this knowledge is not guaranteed to be found and – if at all – hard to extract. Modular neural networks are better suited for this purpose. Domain-ordered by topology, rule extraction is performed module by module. This has all the benefits of a divide-and-conquer method and opens the way to structured design. This paper discusses a next step in this direction by illustrating the potential of base functions to design the neural model.
    Original languageUndefined
    Title of host publicationProceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)
    EditorsHendrik Blockeel, Marc Denecker
    Place of PublicationLeuven, Belgium
    Number of pages2
    Publication statusPublished - Oct 2002
    Event14th Belgium-Dutch Conference on Artificial Intelligence, BNAIC 2002 - Leuven, Belgium
    Duration: 21 Oct 200222 Oct 2002
    Conference number: 14


    Conference14th Belgium-Dutch Conference on Artificial Intelligence, BNAIC 2002
    Abbreviated titleBNAIC


    • EWI-1773
    • METIS-207392
    • IR-43784

    Cite this