Abstract
This paper investigates the use of genetic algorithms (GA) in the design and implementation of fuzzy logic controllers (FLC) for incubating egg. 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 takes too long in solving the problem. This paper 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 which 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 1.0081 with GA (FGA).
Highlights
Automatic control has played an important role in the advance of engineering and science
This paper investigates the use of genetic algorithms (GA) in the design and implementation of fuzzy logic controllers (FLC) for incubating egg
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
Summary
Automatic control has played an important role in the advance of engineering and science. While modern control theory [1] has been easy to practice, fuzzy logic controllers (FLC) have been rapidly gaining popularity among practicing engineers. One of the problems with the incubate-egg systems occurs in the design of the temperature controllers. It is very easy to overheat the eggs in incubators and difficult to maintain proper humidity These controllers are designed with a high sensitivity to disturbance signals. It is important to select the accurate membership functions for temperature setting an incubate egg systems. Conventional FLC used membership function generated by human operators, who have been manually designing the membership function of FLC To satisfy such requirements including one common weakness where the membership function selection process is done with trial and error, it runs step by step, which is too long in completing the problem. We use GA to tune the membership function for terms of each fuzzy variable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
