The Control Strategy of Permanent Magnet Synchronous Motor Based on Neural Network

In view of the limitations of the traditional Hash search algorithm in research on the control strategy of the permanent magnet synchronous motor, a research scheme of research on the control strategies based on Neural network was proposed. Firstly, the influencing factors is accurately located through nerve fiber theories, and the indicators is reasonably divided to reduce interference, and the Neural network is used to construct a research scheme for research on the control strategies. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Hash search algorithm in terms of research accuracy and processing time of influencing factors of research on the control strategies, and has obvious advantages. The research of research on the control strategies plays an extremely important role in permanent magnet synchronous motor, which can accurately predict and optimize the growth characteristics and product generation of permanent magnet synchronous motor. However, the traditional Hash search algorithm has some limitations in solving the problem of electric machine control simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of research on the control strategies based on Neural network to better solve this problem. The scheme accurately locates the influencing factors through nerve fiber theories, so as to determine the division of indicators, and uses the Neural network to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on Neural network in research on the control strategy of the permanent magnet synchronous motor can better solve the limitations of traditional Hash search algorithm and improve the simulation accuracy and efficiency.