On a Learning Method of the SIC Fuzzy Inference Model with Consequent Fuzzy Sets

Abstract

In the conventional fuzzy inference models, various learning methods have been proposed. It is generally impossible to apply the steepest descent method to fuzzy inference models with consequent fuzzy sets, such as Mamdani's fuzzy inference model because it uses min and max operations in the inference process. Therefore, the Genetic Algorithm (GA) was useful for learning of the above model. In addition, it has been also proposed the method for obtaining fuzzy rules of the fuzzy inference models unified max operation from the steepest descent method by using equivalence property. On the other hand, Single Input Connected (SIC) fuzzy inference model can set a fuzzy rule of 1 input 1 output, so the number of rules can be reduced drastically. In the learning method of SIC model unified max operation with consequent fuzzy sets, GA was only applied to the model. Therefore, this paper proposes a leaning method of SIC model unified max operation with consequent fuzzy sets by using equivalence. Moreover, the proposed method is applied to a medical diagnosis and compared with the SIC model by using GA.