An Evaluation Model of Urban-Rural Exchange Teachers in Elementary School Based on Optimal Control Neural Network

In this paper, an optimal control neural network algorithm is used to conduct an in-depth study and analysis of the evaluation of elementary school urban-rural exchange teachers, and an optimal control neural network evaluation model is designed and applied to the actual elementary school urban-rural exchange process. A tracking controller is designed to track the target trajectory for a typical second-order nonlinear system where the system model is partially unknown and the internal state of the system is unpredictable. The neural network observer is first designed using the input and output information of the system to approximate the drift dynamics of the system on the one hand and estimate the internal state information of the system on the other hand; then, based on the estimated system state, the sliding mode tracking controller is designed to achieve tracking of the established target trajectory. It is found that the policy implementation is not in place, teachers are not motivated to exchange and most of them aim at position promotion, their sense of responsibility is weak, and they cannot treat the matter of exchange seriously and correctly, thus cannot play their role in the exchange school. The ways to prevent and alleviate the conflict of interest among policy subjects are mainly divided into subjective and objective aspects: first, from the subjective level to arouse the value recognition of multiple interest subjects, including the government, should play the function of policy guidance and exhortation and clarify the value orientation of the policy; schools should cultivate the moral quality and professionalism of teachers and strengthen ideological mobilization; and teachers should improve their ideological awareness and consciously change their willingness to exchange. Establishing teachers’ status as civil servants, paying attention to teachers’ professional development and safeguarding the interests of rotating teachers to increase teachers’ motivation to exchange and rotate, creating a good social opinion environment, providing a good inflow school support environment for rotating teachers, and building a cloud platform for sharing high-quality educational resources create a good policy implementation environment to ensure that the teacher exchange and rotation policy is effectively implemented to achieve its goals of balancing teacher deployment. Although gradient-based methods can achieve high training accuracy, the performance of the backpropagation algorithm may be unsatisfactory when applied to test data, that is, if the size of the training data is not large enough.