New Optimal Approach for the Identification of Takagi-Sugeno Fuzzy Model

A new and efficient approach is presented that lead to improve the local and global approximation (matching) and modelling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S model can not be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S model because this type of membership function has been widely used during the last two decades in the stability, controller design of fuzzy systems and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S fuzzy model with optimized performance in approximating nonlinear functions. Illustrative examples are chosen to evaluate the robustness and performance of the proposed method and the high accuracy obtained in approximating nonlinear systems locally and globally in comparison with the original T-S model. It is worth noting that proposed algorithm achieve a remarkable performance in comparison with the original T-S model.