Adaptivity in a Hierarchical Fuzzy Model

Robert J. Hammell, Thomas Sudkamp

Research output: Contribution to conferencePaperpeer-review

Abstract

A hierarchical architecture for fuzzy modeling and inference has been developed to allow adaptation based on system performance feedback. A general adaptive algorithm is presented and its performance examined for three types of adaptive behaviour: continued learning, gradual change, and drastic change. In continued learning, the underlying system does not change and the adaptive algorithm utilizes the real time data and associated feedback to improve the accuracy of the existing model. Gradual and drastic change represent fundamental alterations to the system being modeled. In each of the three types of behaviour, the adaptive algorithm has been shown to be able to reconfigure the rule bases to either improve the original approximation or adapt to the new system.

Original languageEnglish
Pages151-156
Number of pages6
DOIs
StatePublished - 1995
Event3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society - College Park, MD, USA
Duration: Sep 17 1995Sep 20 1995
Conference number: 3

Conference

Conference3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society
Abbreviated titleISUMA - NAFIPS'95
CityCollege Park, MD, USA
Period9/17/959/20/95

ASJC Scopus Subject Areas

  • General Computer Science
  • General Mathematics

Keywords

  • Adaptive algorithm
  • Adaptive systems
  • Artificial intelligence
  • Associative memory
  • Computer architecture
  • Computer science
  • Feedback
  • Fuzzy sets
  • Fuzzy systems
  • Real time systems

Disciplines

  • Computer Sciences
  • Engineering
  • Mathematics

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