Semi-polynomial Takagi-Sugeno-Kang Type Fuzzy System for System Identification and Pattern Classification

In this study a new type of Takagi-Sugeno-Kang (TSK) type fuzzy system with dimension reduction section at the input stage called Semi-polynomial data Mapping Fuzzy Inference System (SPMFIS) is proposed. In the proposed method a semi-polynomial feature map is used to transform the input variables to new extracted features with low dimensions. At the next step, these new features are used as the input vector of ANFIS structure. Also gradient descent algorithm is chosen for training parameters of ANFIS and SPM parts of the proposed method. In order to evaluate the capability of the proposed method, its applications in classification of some different benchmark data sets, system identification, and time series prediction have been studied. The results show that the proposed method performs better than the conventional models in classification, identification and time series prediction.