Weight-feeder control for plastic extruder using fuzzy Genetic Algorithms

This study investigates the use of Genetic Algorithms (GA) to the design and implementation of Fuzzy Logic (FLC) for weigh-feeder control. A fuzzy logic is fully defined by its membership function (MF). What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in solving the problem. This research develops a system that may help users to determine the membership function of FLC using the GA optimization for the fastest processing in solving the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, we will get a better and exact result; the value of overshot is decreasing from 1.2800 for FLC without GA, to 1.0070 with GA (FGA).