Abstract
This paper is concerned with a reinforced rule-based fuzzy model and its design realized with the aid of fuzzy clustering. The objective of this study is to develop a new design methodology of constructing incremental fuzzy rules formed through fuzzy clustering. The proposed model consists of four functional modules. The premise part of the fuzzy rules involves membership functions designed with the aid of the Fuzzy C-Means (FCM) clustering algorithm. The consequent part comprises local models (linear functions). The parameters of the local models are estimated by Weighted Least Squares (WLS). In the inference part, after determining the error associated with each fuzzy rule, the rule with the highest error is identified and refined. The selected rule is split into two or more specialized more detailed rules providing a better insight and detailed view into the system. These new rules are formed with the aid of the context-based Fuzzy C-Means (C-FCM) clustering. Along with the refinement of the rule, the linear conclusion part can be also refined by admitting quadratic functions. The effectiveness of the proposed rule-based model is discussed and illustrated with the aid of some numeric studies including both synthetic and machine learning data.