Direct torque control based on FNN and optimization

The paper is proposed for fuzzy neural net based on genetic algorithm adjusting asynchronous motor direct torque control system. Fuzzy neural network training usually adopts the back-propagation study algorithm (BP). Yet the training time of BP algorithm is longer and it is easy to fall into problems such as local minima and so on. We adopt the genetic algorithm in order to optimize the weight of the fuzzy neural network and the parameters of the membership function. We make up for the defect of the fuzzy neural network by adopting the BP algorithm. This made the speed of the arithmetic faster and helped to achieve global optimization.