Learning of Fuzzy Rules and its Application to Nonlinear Systems Modeling
模糊规则的学习及其在非线性系统建模中的应用

This paper discusses fuzzy inference systems and their application to nonlinear system modeling. The paper presents a new algorithm for learning fuzzy rules. The algorithm first partitions input data into some clusters by competitive learning, then determines the decision margins for each input cluster, and finally, learns the fuzzy rules for each input local region. The paper proposes and adaptive fuzzy inference method for the fuzzy rules. Examples are provided to demonstrate the presented learning algorithm and the computing results show that the method of the paper is superior to those in the reference.