Genetic algorithms in the identification of fuzzy compensation system
Yo‐Ping Huang, Kaiquan Shi · 2002
In this paper the adaptive macroevolution genetic algorithms are proposed to identify the type-2 fuzzy compensator. We use the type-2 fuzzy model to remedy the prediction output from a grey system. Through altering the operating order of the three major operators in genetic algorithms, the proposed GAs have the merit of keeping the best solution until finding a better one. The way the genetic algorithms exploited to optimize the fuzzy model is well explained. The superiority of the adaptive macroevolution genetic algorithms to the simple ones is discussed and an example is given to verify our viewpoints. Several simulation results are presented to illustrate the effectiveness of genetic algorithms in optimizing the fuzzy compensator.