Turbojet modeling and simulation in wind milling based on ANFIS

The deficiency of weak generalization ability in the case of small sample size has restricted neural network's application in modeling of wind milling. Based on ten samples experimental data of wind milling, a neural network's model of wind milling is built. By incorporating priori knowledge of dynamic and static state of rotor, similar parameters and the relationship between residual power and acceleration, the training samples numbers can be decreased step by step. Finally a neural network model of wind milling, which has a good generalization ability, can be set up in the case of just one training sample. The incorporation of priori knowledge greatly improves neural network's generalization ability. Results of the simulation prove that the method is simple and effective.