Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability

Abstract

Current studies of type-2 neural fuzzy systems (FSs) (NFSs) primarily focus on building a fuzzy model with high accuracy and disregard the interpretability of fuzzy rules. This paper proposes a data-driven interval type-2 (IT2) NFS with improved model interpretability (DIT2NFS-IP). The DIT2NFS-IP uses IT2 fuzzy sets in its antecedent part and intervals in its zero-order Takagi-Sugeno-Kang-type consequent part for rule form simplicity. The initial rule base is generated by a self-splitting clustering algorithm in the input-output space. The DIT2NFS-IP uses a two-phase parameter-learning algorithm to design an accurate model with improved rule interpretability. In the first phase, a new cost function that considers both accuracy and transparent fuzzy set partition is defined. The antecedent and consequent parameters are learned through gradient descent and rule-ordered recursive least squares algorithms, respectively, to achieve cost function minimization. The second phase performs a fuzzy set reduction, followed by consequent parameter learning to improve accuracy. Comparisons with different type-1 and type-2 FSs in five databased modeling and prediction problems verify the performance of the DIT2NFS-IP in both model accuracy and interpretability.