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

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    Abstract

    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
    Pages507-508
    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

    Conference

    Conference14th Belgium-Dutch Conference on Artificial Intelligence, BNAIC 2002
    Abbreviated titleBNAIC
    CountryBelgium
    CityLeuven
    Period21/10/0222/10/02

    Keywords

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

    Cite this

    van der Zwaag, B. J., Slump, C. H., & Spaanenburg, L. (2002). Process identification through modular neural networks and rule extraction (extended abstract). In H. Blockeel, & M. Denecker (Eds.), Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02) (pp. 507-508). Leuven, Belgium.