Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
In human learning, knowledge representation for a little the quantity of learning is different from that for a lot of quantity of learnning. We proposed a learning that switches knowledge representation. Our emotions influence switching of knowledge representation of human. In the previous paper, therefore, we applied the incorrect ratio to Russell's circumplex model by using a fuzzy rule to calculate the degree of emotions, by which the threshold of the incorrect ratio for switching knowledge representation was increased or decreased. We simulated using the iris data set. The result showed emotions settled to joy, and the threshold kept on increasing. In this paper, we propose two methods of controlling the threshold, one is a method for greatly decreasing the threshold if there is even a few incorrect answers after the middle stage of inference and the other is a method for considering saturated state if the same emotion continues. These two methods are compared and examined by simulating the data other than iris data set.
