On interpretability of fuzzy models based on conciseness measure

Fuzzy modeling is a method to describe input-output relationships of unknown systems using fuzzy inference. Interpretability is one of the indispensable features of fuzzy models. This paper discusses the interpretability of fuzzy model with/without prior knowledge about the target system. Without prior knowledge, conciseness of fuzzy model helps humans to interpret its 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. This paper defines the conciseness of fuzzy models, and formulates the conciseness measure. Experimental results show that the obtained 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