New identification method for a fuzzy model

This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures are suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.