Selection of Input Variables of Fuzzy Model Using Genetic Algorithm with Quick Fuzzy Inference
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
Interpretability is one of the indispensable features of fuzzy models. This paper discusses the interpretability of fuzzy models with/without prior knowledge about the target system. Without prior knowledge, conciseness of fuzzy models helps humans to interpret their input-output relationships. In the case where a human has the knowledge in advance, an interpretable model could be the one that explicitly explains his/her knowledge. Experimental results show that the concise model has the essential interpretable feature. The results also show that human's knowledge changes the most interpretable model from the most concise model.