Name
Abstract | ||
|---|---|---|
In this paper, a fuzzy Petri net model has been proposed based on generalized weighted fuzzy production rule. Generalization has been made on the basis of inclusion of the intuitionistic fuzzy set with the associated intuitionistic fuzzy weight as input values. Such complex model may often give rise to a fuzzy Petri net model based on a complex logical operator resulting in higher computational complexity. For this reason, a dual fuzzy Petri net model of the form (N, N') has been proposed based on a simple weighted logical operator which confirms lesser computational complexity. Approximate reasoning algorithms have been proposed based on this dual structure of a fuzzy Petri net. A numerical example concerning a train traffic problem justifies the relevance of the approach in a real-life problem. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | generalized weighted fuzzy production rule, generalized weighted intuitionistic fuzzy Petri net, knowledge representation, approximate reasoning, weighted composite average operator |
Field | DocType | ISSN |
Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy mathematics,Stochastic Petri net,Artificial intelligence,Fuzzy number,Machine learning | Conference | 1544-5615 |
Citations | PageRank | References |
1 | 0.40 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zbigniew Suraj | 1 | 501 | 59.96 |
| Sibasis Bandyopadhyay | 2 | 2 | 0.75 |