A New Split and Merge Algorithm for Structure Identification in Takagi-Sugeno Fuzzy Model
Ahmad Kalhor, Babak Nadjar Araabi, Caro Lucas · Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007) · 2007
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.