A Case Study of Data-driven Interpretable Fuzzy Modeling

An approach to identify interpretable fuzzy models from data is proposed.Interpretabil- ity,which is one of the most important features of fuzzy models,is analyzed first.The number of fuzzy rules is determined by fuzzy cluster validity indices.A modified fuzzy clustering algorithm, combined with the least square method,is used to identify the initial fuzzy model.An orthogonal least square algorithm and a method of merging similar fuzzy sets are then used to remove the re- dundancy of the fuzzy model and improve its interpretability.Next,in order to attain high accuracy, while preserving interpretability,a constrained Levenberg-Marquardt method is utilized to optimize the precision of the fuzzy model.Finally,the proposed approach is applied to a PH neutralization process,and the results show its validity.