Visualization of neural-network gaps based on error analysis

Mehmed M. Kantardzic, Alaaeldin A. Aly, Adel S. Elmaghraby

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)


    Presented in this paper is a new methodology for detection of neural-network gaps (NNG's) based on error analysis and the visualization that is applicable for n-dimensional I/O domain. The generalization problem in artificial neural networks (ANN) training is analyzed and the concept of NNG's is introduced. The NNG's are highly undesirable in ANN generalization and methods for detecting, analyzing, and eliminating them are necessary. Previous methods for NNG detection, based on two-dimensional (2-D) and three-dimensional (3-D) visualization, were not applicable for ANN's with more than three inputs. Experiments demonstrate advantages of this new methodology, which allows better understanding of the NNG phenomena using a quantitative approach.

    Original languageEnglish
    Pages (from-to)419-426
    Number of pages8
    JournalIEEE Transactions on Neural Networks
    Issue number2
    Publication statusPublished - Dec 1 1999


    • Error analysis
    • Neural-network gaps
    • Neural-network generalization
    • Visualization

    ASJC Scopus subject areas

    • Software
    • Computer Science Applications
    • Computer Networks and Communications
    • Artificial Intelligence


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