Process identification through modular neural networks and rule extraction

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

    73 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 publicationComputational Intelligent Systems for Applied Research, Proceedings of the 5th International FLINS Conference
    EditorsDa Ruan, Pierre D'hondt, Etienne E. Kerre
    Place of PublicationSingapore
    PublisherWorld Scientific
    Number of pages10
    ISBN (Print)981-238-066-3
    Publication statusPublished - Sep 2002

    Publication series

    PublisherWorld Scientific


    • METIS-207183
    • IR-43692
    • EWI-1416

    Cite this