Based on Clustering Algorithm Expert System of Self-learning Rule Base

Through data analysis method to obtain the internal connection of the data set, and extract different characteristics from it, and provide a strategy for the self-learning and acquisition of the expert system rule base. The article analyzes the composition and operating principle of the expert system, and selects the clustering algorithm to design the process of self-learning rules, and then obtains the rule base of the expert system, trying to solve the problem of difficulty in obtaining the empirical reasoning rule base. A circuit fault diagnosis example is selected to verify the feasibility of the method. After simulation tests, it is proved that the algorithm can realize self-learning rules, and an expert system rule base is initially formed, and fault information can be reasoned and diagnosed to simulate or replace empirical rules to a certain extent.