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dc.creatorChia-Feng Juangen_US
dc.creatorChi-You Chenen_US
dc.description.abstractCurrent studies of type-2 neural fuzzy systems (FSs)(NFSs) primarily focus on building a fuzzy model with highaccuracy and disregard the interpretability of fuzzy rules. Thispaper proposes a data-driven interval type-2 (IT2) NFS withimproved model interpretability (DIT2NFS-IP). The DIT2NFS-IPuses IT2 fuzzy sets in its antecedent part and intervals in itszero-order Takagi–Sugeno–Kang-type consequent part for ruleform simplicity. The initial rule base is generated by a self-splittingclustering algorithm in the input–output space. The DIT2NFS-IPuses a two-phase parameter-learning algorithm to design an accuratemodel with improved rule interpretability. In the firstphase, a new cost function that considers both accuracy andtransparent fuzzy set partition is defined. The antecedentand consequent parameters are learned through gradient descentand rule-ordered recursive least squares algorithms, respectively,to achieve cost function minimization. The second phase performsa fuzzy set reduction, followed by consequent parameter learningto improve accuracy. Comparisons with different type-1 andtype-2 FSs in five databased modeling and prediction problemsverify the performance of the DIT2NFS-IP in both model accuracyand interpretability.en_US
dc.relationIEEE TRANSACTIONS ON CYBERNETICS, Volume 43, Issue 6.en_US
dc.subjectFuzzy neural networks (FNNs), interpretable fuzzy systems (FSs)en_US
dc.subjectsequence predictionen_US
dc.subjecttype-2 FSsen_US
dc.titleData-Driven Interval Type-2 Neural FuzzySystem With High Learning Accuracy andImproved Model Interpretabilityen_US
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