Rule-base self-generation and simplification for data-driven fuzzy models

A rule-base self-extraction and simplification method is proposed to establish interpretable fuzzy models from numerical data. A fuzzy clustering technique is used to extract the initial fuzzy rule-base. The number of fuzzy rules is determined by the proposed fuzzy partition validity index. To reduce the complexity of fuzzy models without decreasing the model accuracy significantly, some approximate similarity measures are presented and a parameter fine-tuning mechanism is introduced to improve the accuracy of the simplified model. The simplified fuzzy model has good balance between accuracy and transparency.