Abstract
A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training.
| Original language | English |
|---|---|
| Pages (from-to) | 41-45 |
| Number of pages | 5 |
| Journal | Journal of Beijing Institute of Technology (English Edition) |
| Volume | 17 |
| Issue number | 1 |
| Publication status | Published - Mar 2008 |
Keywords
- Fuzzy Petri nets
- Fuzzy reasoning
- Knowledge learning
- Knowledge representation