Discrete Analogue for a Class of Impulsive Cohen–Grossberg Neural Networks with Asynchronous Time-Varying Delays

Personalized Teaching Strategy of University Ideology Course Based on Lagrange Neural Network and Big Data Technology

Individualized instruction is a type of educational principle. On the one hand, it necessitates the creation of individualized teaching resources, courses, and methods. Students, on the other hand, require a high level of autonomy and the ability to make personalized plans based on their own cognitive characteristics and needs. The big data (BD) era opens up new possibilities for IE (ideological education) work in universities, but it also poses some challenges. IE development will be greatly aided by recognizing opportunities to meet challenges and optimizing and integrating PL (political lesson) resources. The LNN (Lagrange neural network) model has been established. The simulation results show that the LNN network can converge to the optimal solution quickly and effectively and then reconstruct sparse signals. Individualized college PL instruction using LNN and BD technology helps students communicate more effectively and improves the pertinence, immediacy, and positivity of IE.

Discrete analogue of impulsive recurrent neural networks with both discrete and finite distributive asynchronous time-varying delays

This paper studies the dynamical characteristics of discrete analogue of impulsive recurrent neural networks with both discrete and finite distributed asynchronous time-varying delays. Firstly, the discrete impulsive system of the corresponding continuous-time model is reformed by impulsive maps and semi-discrete method. Secondly, by employing a famous delay impulsive differential inequality, several novel sufficient conditions are derived to ensure the uniqueness of equilibrium point and its global exponential stability in Lagrange sense for the discussed discrete-time impulsive system. Meanwhile, it is illustrated that the discrete-time analogue retains the uniqueness of equilibrium point of the corresponding continuous-time model, and some corollaries follow. Finally, one example is given to demonstrate the validity of our obtained results.