Optimization of Broad Learning System Based on Fuzzy C-Means Clustering

Aiming at the problems of traditional neuro-fuzzy models that rely on manually defined fuzzy rules and are prone to “rule explosion,” this paper proposes a Fuzzy Broad Learning System based on Fuzzy C-Means clustering (FCM-Fuzzy BLS). In the proposed framework, the FCM algorithm is employed to automatically generate fuzzy rules, thereby eliminating subjective rule design and enhancing model interpretability. The fuzzy membership degrees obtained through clustering serve as the basis for constructing the mapping between input features and fuzzy rules, ensuring adaptive rule generation. Meanwhile, the broad learning system (BLS) structure is integrated to enable efficient incremental learning and fast network expansion without retraining from scratch. By combining the fuzzy inference mechanism with BLS’s linear mapping and pseudo-inverse optimization, the proposed model achieves both high computational efficiency and robust generalization performance. Experimental results on multiple benchmark datasets demonstrate that the FCM-Fuzzy BLS not only alleviates the rule-explosion problem but also significantly improves accuracy, convergence speed, and scalability compared with conventional fuzzy neural networks.