SimBAC‐NFS: Similarity‐Based Aggregation and Calibration of Neuro‐Fuzzy Systems

Ensemble learning improves the accuracy of the neuro‐fuzzy systems but compromises their interpretability because of the separate, independent rule bases. In this paper, we propose SimBAC‐NFS, which combines the variance reduction of bagging with the residual learning of boosting to produce a single interpretable model and rule base. The model assesses similarity and identifies rules describing similar input regions across all learners, merges them into consistent membership functions and refits the consequent linear parameters on the full training data. SimBAC‐NFS achieves the lowest error on eight of the nine datasets among single and classical fuzzy ensemble models on nine engineering regression benchmarks. Against unconstrained black‐box baselines such as XGBoost, LightGBM and Random Forest, it wins outright on the two datasets with the smoothest, structurally simplest responses. On the remaining seven, it accepts a bounded accuracy gap in exchange for a single rule base that a domain expert can read and audit directly.