A Learning Method of Non-Differential SIC Fuzzy Inference Model Using Genetic Algorithm and Its Application to a Medical Diagnosis

Abstract

There are typical learning methods for fuzzy inference models such as the steepest descent method, genetic algorithm, etc. It is generally impossible to apply the steepest descent method to fuzzy inference models with consequent fuzzy sets, such as Mamdani’s fuzzy inference models that use min and max operations. Therefore, genetic algorithm will be useful for the above model. However, the computational complexity of genetic algorithm is much larger than the steepest descent method. Also, since all input items are set to the antecedent parts in typical fuzzy inference model, the number of rules increases exponentially. Moreover, considering the computational complexity of genetic algorithm, it will not be necessarily suitable. On the other hand, Single Input Connected (SIC) fuzzy inference model sets the fuzzy rule of 1 input 1 output, so the number of rules can be reduced drastically. The consequent parts of the conventional SIC model were real number although linguistic interpretation is possible and easy to understand if the consequent parts are fuzzy sets. Therefore, we propose a new SIC model which extends real number of the consequent parts to fuzzy sets, and the fuzzy rules are derived by using genetic algorithm. In addition, it is applied to a medical diagnosis and compared with the conventional fuzzy inference models.