FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, Droprule, and Powerball Adabelief

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization c-means clustering, Powerball AdaBelief, integrates proposed AdaBelief to expedite stabilize parameter optimization. Extensive experiments on 22 datasets various sizes dimensionalities validated superiority of FCM-RDpA over MBGD-RDA, especially when feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that using augmented features both antecedents consequents rules.