Second order Takagi-Sugeno fuzzy model with domain adaptation for nonlinear regression

In the regression analysis, Takagi-Sugeno fuzzy model gives a way of exploiting fuzzy logic to tackle nonlinear issues. However, the general Takagi-Sugeno fuzzy model encounters challenges when facing second order regression problems because of its insufficient fitting ability. In this study, the second order Takagi-Sugeno fuzzy model called TS² fuzzy model is proposed to extend the application scope of the original model. Moreover, domain adaptation in transfer learning is applied to the proposed model by using space transformation. It aims to further reduce the model's cumulative error. The experimental results indicate that the proposed model has a better performance with not much extra processing time when dealing with second order nonlinear regression tasks.