A Design of Incremental Granular Model Using Context-Based Interval Type-2 Fuzzy C-Means Clustering Algorithm

Keun-Chang KWAK  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.1   pp.309-312
Publication Date: 2016/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDL8076
Type of Manuscript: LETTER
Category: Biocybernetics, Neurocomputing
incremental granular model,  context-based type-2 fuzzy c-means clustering,  linear regression,  linguistic model,  

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In this paper, a method for designing of Incremental Granular Model (IGM) based on integration of Linear Regression (LR) and Linguistic Model (LM) with the aid of fuzzy granulation is proposed. Here, IGM is designed by the use of information granulation realized via Context-based Interval Type-2 Fuzzy C-Means (CIT2FCM) clustering. This clustering approach are used not only to estimate the cluster centers by preserving the homogeneity between the clustered patterns from linguistic contexts produced in the output space, but also deal with the uncertainty associated with fuzzification factor. Furthermore, IGM is developed by construction of a LR as a global model, refine it through the local fuzzy if-then rules that capture more localized nonlinearities of the system by LM. The experimental results on two examples reveal that the proposed method shows a good performance in comparison with the previous works.