IMPROVED FUZZY KNOWLEDGE REPRESENTATION AND RULE EVALUATION USING FUZZY PETRI NETS AND DEGREE OF SUBSETHOOD

In this article a variation of fuzzy Petri net (FPN) model is proposed to accommodate for the possibility of mapping fuzzy production rule ( FPR) having different threshold values in their propositions into FPN. The purpose of assigning a different threshold value for each proposi tion in the FPR and of using the rule checking and evaluation method p roposed here is to prevent misfiring of the rule, which can result wit h other methods; the purpose of having variation of FPN model is to ca pture and represent more information of FPR in the FPN model. The rule checking and evaluation method is an enhancement of the approach prop osed by Yeung (D. S. Yeung et al., Proceedings. of the 6th Internation al Conference on System Research Informatics and Cybernetics, Germany, 1992). As mentioned by the authors, the degree of subsethood between two vectors is the basis of the method. The subsethood method will fir st be used to make certain that each input value for the proposition i n the antecedent is greater than or equal to its corresponding thresho ld value. When such condition holds, the subsethood method is used to infer the degree of truth of the consequent of the rule. An enhanced f uzzy reasoning algorithm is included. Comparison of this method with o ther methods is presented. Future research work in determining accepta ble threshold values and certainty factors is addressed. (C) 1994 John Wiley and Sons, Inc.