Improved fuzzy approximation accuracy using a linear TSK approach

We propose a new Takagi-Sugeno-Kang (TSK) approach for fuzzy function approximation. We use a novel linear TSK design to define the rule base and obtain the membership functions for the inputs and outputs of a given system. Unlike traditional TSK, the output is obtained as a weighted sum of linear functions of the inputs. We provide an upper bound on the approximation error for this class of fuzzy systems achievable using a reduced number of membership functions. To demonstrate the new approach, we apply it to a numerical example.