A New Split and Merge Algorithm for Structure Identification in Takagi-Sugeno Fuzzy Model

Authors:
Ahmad Kalhor;Babak N. Araabi;Caro Lucas
Affiliations:
-;-;-
Venue:
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
Year:
2007

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Abstract

In this paper a novel algorithm for structure identification of Takagi-Suegno (TS) fuzzy models based on split and merge clustering is purposed. In this algorithm, by using a sequential split procedure on data space, initial Gaussian functions as constructor blocks are created. By merging these initial blocks, new composite validity functions for locally linear models with a high degree of flexibility are estimated. The proposed algorithm results in TS-type locally linear fuzzy models with an abstract structure as well as high generalization. Desirable performance of this algorithm is illustrated at case study section.