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Fuzzy knowledge representation and reasoning using a generalized fuzzy petri net and a similarity measure

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Abstract

In the study of weighted fuzzy production rules (WFPRs) reasoning, we often need to consider those rules whose consequences are represented by two or more propositions connected by “AND” or “OR”. To enhance the representation capability of those rules, this paper proposes two types of knowledge representation parameters, namely, the input weight and the output weight, for a rule. A Generalized Fuzzy Petri Net (GFPN) is also presented for WFPR reasoning. Furthermore, this paper gives a similarity measure to improve the evaluation method of WFPRs and the multilevel fuzzy reasoning in which the consequences and their certainty factors are deduced synchronously by using a GFPN.

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Authors and Affiliations

  1. College of Mathematics and Computer Sciences, Hebei University, Baoding, 071002, China

    Ming-Hu Ha

  2. College of Science, Hebei University of Science and Technology, Shijiazhuang, 050018, China

    Yan Li

  3. Hebei Radio and TV University, Shijiazhuang, 050071, China

    Xiao-Feng Wang

Authors
  1. Ming-Hu Ha
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  2. Yan Li
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  3. Xiao-Feng Wang
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Correspondence to Ming-Hu Ha.

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Ha, MH., Li, Y. & Wang, XF. Fuzzy knowledge representation and reasoning using a generalized fuzzy petri net and a similarity measure. Soft Comput 11, 323–327 (2007). https://doi.org/10.1007/s00500-006-0084-4

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  • DOI: https://doi.org/10.1007/s00500-006-0084-4

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