Data-based fuzzy system modeling and identification

Fuzzy logic is a general computation system based on the fuzzy set theory, which is inspired by human thinking. This system is based on the relations between logical expressions and linguistic variables. One of the most important advantages in terms of engineering is that it does not need a mathematical model of the system of interest. The main problem is to determine the most suitable values of its parameters so as to perform the task expected from it. In this study, it has investigated that the parameters of commonly used TS type fuzzy system are determined based on input / output data at hand. First, the input-output variables of the fuzzy system are determined by the method given in this study, and the variables of the input are fuzzificated by the homogeneous distributed membership functions in the related input space. Thus, the premise/antecedent parameters of the fuzzy system are determined. Then, the consequent/rule parameters of the fuzzy system are determined based on the input-output sample data with the least square estimation (LSE) method. In this study, this method is discussed on the modeling of five different dynamical systems with fuzzy systems of TS type. The results show that the method can be used effectively if the designer has input-output samples.