Auto Generation of Fuzzy Model using Genetic Algorithm and Delta Rule

摘要

This paper deals with an automatic generation algorithm of a fuzzy model using the genetic algorithm and the delta rule, The fuzzy inference is applied to the various problems. However, the determination of the membership functions is a difficult problem, because the determination depends on human experts. The auto-tuning methods of the fuzzy model have been proposed to develop the time-consuming operation by human experts. Nevertheless, the auto-tuning methods have a weak point, such that it is difficult for human experts to set the initial conditions of the system. The convergence of tuning depends on the initial conditions, which are determined by the scale and parameters of system. So, we propose an AFUGA system (Auto Fuzzy Tuning Method using Genetic Algorithm). This method brings a minimal and optimal structure of the fuzzy model. This general system can be applied to the robotic motion control, sensing and recognition problems and so on. In this paper, we show the validity of the AFUGA system by simulation.