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The construction of a fuzzy inference network by extension of the rule inference network

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Abstract

Fuzzy logic can bring about inappropriate inferences as a result of ignoring some information in the reasoning process. Neural networks are powerful tools for pattern processing, but are not appropriate for the logical reasoning needed to model human knowledge. The use of a neural logic network derived from a modified neural network, however, makes logical reasoning possible. In this paper, we construct a fuzzy inference network by extending the rule–inference network based on an existing neural logic network. The propagation rule used in the existing rule–inference network is modified and applied. In order to determine the belief value of a proposition pertaining to the execution part of the fuzzy rules in a fuzzy inference network, the nodes connected to the proposition to be inferenced should be searched for. The search costs are compared and evaluated through application of sequential and priority searches for all the connected nodes.

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Acknowledgements

This paper was supported by research funds of Namseoul University.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, ChonBuk National University, 664-14, 1Ga, DeokJin-Dong, JeonJu, ChonBuk, 561-756, Korea

    Mal-rey Lee

  2. Department of Multimedia, Namseoul University, Korea

    Tae-Eun Kim

Authors
  1. Mal-rey Lee
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  2. Tae-Eun Kim
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Correspondence to Mal-rey Lee.

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Lee, Mr., Kim, TE. The construction of a fuzzy inference network by extension of the rule inference network. Neural Comput & Applic 14, 223–228 (2005). https://doi.org/10.1007/s00521-004-0457-5

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  • DOI: https://doi.org/10.1007/s00521-004-0457-5

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