A systematic approach to linguistic fuzzy modeling based on input-output data

Abstract

A novel systematic algorithm to build adaptive linguistic fuzzy models directly from input-output data is presented. Based on clustering and projection in the input and output spaces, significant inputs are selected, the number of clusters is determined, rules are generated automatically, and a linguistic fuzzy model is constructed. Then, using a simplified fuzzy reasoning mechanism, the back-propagation (BP) and least mean squared (LMS) algorithms are implemented to tune the parameters of the membership functions. Compared to other algorithms, the new algorithm is both computationally and conceptually simple. The new algorithm is called the Linguistic Fuzzy Inference (LFI) model.