Fuzzy ARTMAP network with evolutionary learning

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

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

13 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
Externally publishedYes
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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