Abstract
A new, simple and less computational approach is presented to improve the local and global modeling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main drawback of T-S identification method is that it 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 is popular in industrial control applications. The method developed here can be considered as a generalized version of T-S fuzzy identification method with optimized performance in approximating nonlinear functions. Various examples are chosen to examine the remarkable 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.
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