A novel recursive T-S fuzzy semantic modeling approach for discrete state-space systems - Li, Liang-Qun; Wang, Xiao-Li; Xie, Wei-Xin; Liu, Zong-Xiang (Neurocomputing (Amsterdam), 2019)
•Fuzzy correntropy is constructed.•A novel kernel fuzzy C-regression model clustering based on fuzzy correntropy is proposed.•Modified extended forgetting factor recursive least squares estimator is presented.•The proposed algorithm is applied for maneuvering target tracking. In this paper, we propose a novel recursive Takagi-Sugeno (T-S) fuzzy semantic modeling approach for discrete state-space system. According to the information learning theoretic (ILT), the correntropy can capture the higher moments of the error probability distribution to deal with non-Gaussian noise. Considering the advantages of fuzzy theory and correntropy, fuzzy correntropy is constructed and a novel kernel fuzzy C-regression model clustering based on fuzzy correntropy is proposed to solve the premise parameter identification problem of the T-S fuzzy model. To the identification of the consequent part parameters of the T-S fuzzy model, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is presented. Moreover, to evaluate the performance of the proposed fuzzy model, the proposed T-S fuzzy model is applied to solve the problem of maneuvering target tracking by incorporating the target feature semantic information. Finally, the experiment results show the proposed algorithm can effectively track a maneuvering target, and its performance is better than the exist algorithms, such as interacting multiple model Kalman filter (IMMKF), interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter the (IMMRBPF).