A New Method for Optimizing Fuzzy Membership Function
Yongsheng Zhao, Baoying Li · 2007
the successfulness of fuzzy application depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. In this paper, we propose a new method utilizing Ant Colony Algorithm (ACA) to optimize the fuzzy membership function's parameters, which overcoming the subjectivity and blindness in the process of designing the input or output membership functions. The fuzzy controller, which is optimized by ACA, is applied to a second order model and the simulation results shown a better result.