Growing rule-based fuzzy model developed with the aid of fuzzy clustering

This paper is concerned with a growing 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 concerning incremental fuzzy rules formed through fuzzy clustering. The proposed model consists of three functional components : (a) The premise part of the fuzzy rules involves membership functions designed with the aid of the Fuzzy C-Means (FCM) clustering algorithm. (b) The consequent part comprises local models (linear functions). The parameters of the local models are estimated by running a Weighted Least Square Estimation (WLSE). (c) The process of rule growth in the growing part is concerned with a refinement of the model where a selected rule is split into two or more specialized rules providing a better insight into the system. These new rules are formed with the aid of a so-called context-based Fuzzy C-Means (C-FCM) clustering. 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.