Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller

• Main Author: Ismail, H. Muh Yusuf
• Format: Thesis
• Language: English; English
• Published: 2010
• Subjects: Q Science (General); QA Mathematics
• Online Access: http://eprints.utem.edu.my/id/eprint/13841/1/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller24_pages.pdf; http://eprints.utem.edu.my/id/eprint/13841/2/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller.pdf; http://eprints.utem.edu.my/id/eprint/13841/; http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000061103

This study investigates the use of Genetic Algorithms (GA) to design and implement of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function and control rules generated by human operator. The membership function selection process is done with trial and error and it runs step by step which is too long in solving the underlined the problem. GA have been successfully applied to solve many optimization problems. This research proposes a method that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in solving the problems. The performance of GA can be further improved by using different combinations of selection strategies, crossover and mutation methods, and other genetic parameters such as population size, probability of crossover and mutation rate. 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, the method which proposed is very helpful to determine membership function and it is clear that the GA are very promising in improving the performance of the FLC to find the optimum result.