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S.M. Chen, J.S. Ke, J.F. Chang, "Knowledge Representation Using Fuzzy Petri Nets," IEEE Transactions on Knowledge and Data Engineering, vol. 2, no. 3, pp. 311319, September, 1990.  
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@article{ 10.1109/69.60794, author = {S.M. Chen and J.S. Ke and J.F. Chang}, title = {Knowledge Representation Using Fuzzy Petri Nets}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {2}, number = {3}, issn = {10414347}, year = {1990}, pages = {311319}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.60794}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Knowledge Representation Using Fuzzy Petri Nets IS  3 SN  10414347 SP311 EP319 EPD  311319 A1  S.M. Chen, A1  J.S. Ke, A1  J.F. Chang, PY  1990 KW  fuzzy Petri nets; fuzzy production rule; rulebased system; fuzzy relation; fuzzy reasoning; formal description; upper bound; time complexity; execution time; sprouting tree; computational complexity; knowledge representation; Petri nets VL  2 JA  IEEE Transactions on Knowledge and Data Engineering ER   
A fuzzy Petri net model (FPN) is presented to represent the fuzzy production rule of a rulebased system in which a fuzzy production rule describes the fuzzy relation between two propositions. Based on the fuzzy Petri net model, an efficient algorithm is proposed to perform fuzzy reasoning automatically. It can determine whether an antecedentconsequence relationship exists from proposition d/sub s/ to proposition d/sub j/, where d/sub s/ not=d/sub j/. If the degree of truth of proposition d/sub s/ is given, then the degrees of truth of proposition d/sub j/ can be evaluated. The formal description of the model and the fuzzy reasoning algorithm are shown in detail. The upper bound of the time complexity of the fuzzy reasoning algorithm is O(nm), where n is the number of places and m is the number of transitions. Its execution time is proportional to the number of nodes in a sprouting tree generated by the algorithm only generates necessary reasoning paths from a starting place to a goal place, it can be executed very efficiently.
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