Fuzzy ARTMAP network with evolutionary learning

P. Ramuhalli, R. Polikar, L. Udpa, S. S. Udpa

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

    12 Scopus citations


    Neural networks, particularly the multilayer perceptron, have been used extensively in automated signal classification systems with classification accuracy as the figure of merit. Three important issues that can enhance the utility of these systems are (i) incremental learning, (ii) confidence or reliability measures and (iii) performance improvement through continual learning. This paper investigates these issues using a fuzzy ARTMAP network. A hypothesis testing based algorithm is developed for computing reliability measures, which are fed back to the network for retraining and performance improvement. Implementation results on ultrasonic data are presented.

    Original languageEnglish (US)
    Title of host publicationDesign and Implementation of Signal Processing SystemNeural Networks for Signal Processing Signal Processing EducationOther Emerging Applications of Signal ProcessingSpecial Sessions
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages4
    ISBN (Electronic)0780362934
    StatePublished - Jan 1 2000
    Event25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey
    Duration: Jun 5 2000Jun 9 2000

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ISSN (Print)1520-6149


    Other25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000

    All Science Journal Classification (ASJC) codes

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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