Approximation bound for Fuzzy-Neural Networks with Bell Membership Function

A great deal of research has been devoted in recent years to the designing Fuzzy-Neural Networks (FNN) from input-output data. And some works were also done to analyze the performance of some methods from a rigorous mathematical point of view. In this paper, the approximation bound for the clustering method, which is employed to design the FNN with the Bell Membership Function, is established. The detailed formulas of the error bound between the nonlinear function to be approximated and the FNN system designed based on the input-output data are derived.