Name
Abstract | ||
|---|---|---|
We introduce an automated procedure for extracting information from knowledge bases that contain fuzzy production rules. The knowledge bases considered here are modeled using the high-level fuzzy Petri nets proposed by the authors in the past. Extensions to the high-level fuzzy Petri net model are given to include the representation of partial sources of information. The case of rules with more than one variable in the consequent is also discussed. A reasoning algorithm based on the high-level fuzzy Petri net model is presented. The algorithm consists of the extraction of a subnet and an evaluation process. In the evaluation process, several fuzzy inference methods can be applied. The proposed algorithm is similar to another procedure suggested by Yager (1983), with advantages concerning the knowledge-base searching when gathering the relevant information to answer a particular kind of query |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/91.531771 | IEEE T. Fuzzy Systems |
Keywords | Field | DocType |
high-level fuzzy petri net,high-level fuzzy petri,evaluation process,fuzzy inference method,knowledge base,relevant information,partial source,reasoning algorithm,proposed algorithm,fuzzy production rule,automated procedure,helium,production,knowledge representation,data mining,petri net,information extraction,tree graphs,fuzzy set theory,knowledge based systems,petri nets,fuzzy logic | Neuro-fuzzy,Petri net,Defuzzification,Fuzzy classification,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy number,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 3 | 1063-6706 |
Citations | PageRank | References |
42 | 3.01 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H. Scarpelli | 1 | 42 | 3.01 |
| F. Gomide | 2 | 602 | 29.53 |
| Ronald R. Yager | 3 | 986 | 206.03 |