Knowledge Acquisition of Fuzzy Control System Based on Improved Genetic Algorithm and Neural Networks

Often it is difficult to acquire the requisite knowledge in the design of rule-based control systems. In the study, an advanced knowledge acquisition technique is presented and used in the fuzzy control of hydroelectric generating unit system of hydropower plant. Genetic algorithm is employed to optimize the parameters and rules of fuzzy controller in design controller and real-time control process. In the design, GA is adopted improved algorithm based on general GA and the controlled plant. The improved GA quickens optimizing speed and makes fuzzy controller acquire knowledge effectively. The designed control system can select optimal scale factors, membership functions and control rules efficiently. Simulation results show that the advanced knowledge acquisition technique makes control parameters and rulesof fuzzy controller arrive to optimization and its control performance is superior to conventional controller.