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Connectionism for fuzzy learning in rule-based expert systems

  • Euzzy Logic and Control
  • Conference paper
  • First Online:
Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 1992)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 604))

  • 276 Accesses

Abstract

A novel approach to rule refinement based upon connectionism is presented. This approach is capable of performing rule deletion, rule addition, changing rule quality, and modification of rule strengths. The fundamental algorithm is referred to as the Consistent-Shift algorithm. Its basis for identifying incorrect connections is that incorrect connections will often undergo larger inconsistent weight shift than correct ones during training with correct samples. By properly adjusting the detection threshold, incorrect connections would be uncovered, which can then be deleted or modified. Deletion of incorrect connections and addition of correct connections then translate into various forms of rule refinement just mentioned.

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References

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, University of Florida, 32611, Gainesville, FL

    Fu LiMin

Authors
  1. Fu LiMin
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Editor information

Fevzi Belli Franz Josef Radermacher

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© 1992 Springer-Verlag Berlin Heidelberg

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LiMin, F. (1992). Connectionism for fuzzy learning in rule-based expert systems. In: Belli, F., Radermacher, F.J. (eds) Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. IEA/AIE 1992. Lecture Notes in Computer Science, vol 604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024985

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  • DOI: https://doi.org/10.1007/BFb0024985

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55601-5

  • Online ISBN: 978-3-540-47251-3

  • eBook Packages: Springer Book Archive

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