A generalized TSK model with a novel rule antecedent structure: Structure identification and parameter estimation

TSK fuzzy models are convenient tools for describing complex nonlinear behavior. However, the existing combinatorial antecedent structure in TSK models makes them substantially suffer from the curse of dimensionality. In this work, a novel rule antecedent structure is proposed to design an efficient generalized TSK (GTSK) model by using fewer rules. The new rule antecedent only uses nonlinear variables. Additionally, one more degree of freedom is introduced to design antecedents to cover an antecedent space more efficiently, which further reduces the number of rules. The resultant GTSK model is identified in two stages. A novel recursive estimation based on spatially rearranged data is used to determine the consequent and antecedent variables. Model parameter values are obtained from partitioned antecedent space, which is the result of solving a series of splitting and regression problems.