Knowledge Acquisition Using Fuzzy Inference Unified Max Operation and Its Application to a Medical Diagnosis System

Abstract

Since Mamdani adapted concept of fuzzy inference to control of steam engine experimental equipment, it has been applied in various fields. In the simplified fuzzy inference model, which is one of the conventional fuzzy inference models, a learning method by using teach data and a method for automatically adjusting fuzzy rules has been proposed. In Mamdani's fuzzy inference model, the fuzzy rules were often set by expert on hand. In the fuzzy inference model, the calculations of min and max operations are used in the output derivation process. Therefore, it is difficult to differentiate, and applying it to steepest descent method is generally impossible. On the other hand, fuzzy rules of Mamdani are easier to interpret linguistically than simplified reasoning models, so they are applied in many fields. However, knowledge of fuzzy rules to be used is not necessarily obtained from experts, and development of the method for automatic adjustment in Mamdani type fuzzy inference model is desired. Focusing on the equivalence which is one of the properties of fuzzy inference, inference results are derived using the centroid and area of the consequent fuzzy set, replace the operation of min and max with algebraic product, addition, subtraction. This shows that the fuzzy inference model of Mamdani can be expressed in a form that can be differentiated and learned using the steepest descent method. Although a learning algorithm for a fuzzy inference model using max operation has been proposed, it has not been applied to real systems. In this paper, we apply it to the construction of a medical diagnosis system as one of the applications of real system, and compare the accuracy. Moreover it shows that the learning method can obtain knowledge.