This paper promotes teacher-guided peer learning in education with continuous action iterated dilemma (CAID) based on team leader rotation mechanism. In previous teaching activity models, the learning communication relationship between peers was described as static, but the static relationship will hinder the learning efficiency, which does not match the real world. In addition, in view of the independence of individual students, it is necessary to establish a dynamic model which considers the complex behavior of every student. In this paper, we first propose a team leader rotation mechanism that makes sure each student has the opportunity to become a team leader, which enhances students' sense of participation and improves classroom efficiency. Next, we establish a multi-layer nonlinear student dynamic model based on continuous action iteration dilemma and involved complex and unknown nonlinear environmental factors to fit different environmental influences on different students. Also, in order to demonstrate the convergence of the proposed model, we devise the Lyapunov function as a means of mathematical proof. Through this analysis, we establish the stability of the proposed model and verify its independence from parameters, thereby enhancing its applicability in practical contexts. By incorporating the team leader rotation mechanism proposed in this paper, teachers will be able to ensure diverse student engagement to achieve information consistency, thereby ensuring the effectiveness of the classroom.

The synchronization problem and the dynamics analysis of neural networks have been thoroughly explored, and there have been many interesting results. This paper presents a review of the issues of synchronization problem, the periodic solution and the stability/stabilization with emphasis on the memristive neural networks and reaction-diffusion neural networks. First, this paper introduces the origin and development of neural networks. Then, based on different types of neural networks, some synchronization problems and the design of the controllers are introduced and summarized in detail. Some results of the periodic solution are discussed according to different neural networks, including bi-directional associative memory (BAM) neural networks and cellular neural networks. From the perspective of memristive neural networks and reaction-diffusion neural networks, some results of stability and stabilization are reviewed comprehensively with latest progress. Based on a review of dynamics analysis of neural networks, some applications in creation psychology are also introduced. Finally, the conclusion and the future research directions are provided.