The design of fuzzy controller by means of genetic algorithms and NFN-based estimation technique

In this study, we introduce a neurogenetic approach to the design of fuzzy controllers. The design procedure exploits the technology of Computational Intelligence (CI) focusing on the use of genetic algorithms and neurofuzzy networks (NFN). The crux of the design concerns the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA- based NFN.