Identifying the fuzzy grey prediction model by genetic algorithms

The application of genetic algorithms to the design of the fuzzy grey model is investigated. Based on given past data, the next output from an unknown plant can be predicted by the basic grey model. To better improve the accuracy of the prediction model, a fuzzy controller is designed to determine the quantity of compensation for the output from the grey system. Genetic algorithms are used to optimize the roughly-determined fuzzy model. A test pattern is then fed to the well-tuned system to obtain the compensation quantity through a defuzzification process. The procedures for identifying three different types of fuzzy models are presented. Simulation results from a well-known example are shown to demonstrate that simplicity in modeling and applicability to intelligent prediction systems are the merits of the proposed methodology.