In this paper, different types of learning control schemes are proposed to study the tracking of two-dimensional discrete neural networks. The learning control schemes combine the advantages of impulsive control and iterative learning control strategies, because the impulsive control technique can improve tracking performance rapidly. Further, the event-triggered mechanism is used to determine the impulse time. And an equivalent system is proposed by constructing a trigger function, which is used to get over the difficulties in the theoretical analysis. Then learning control schemes are designed in line with the equivalent system, and some sufficient conditions are proposed to guarantee the convergence of the tracking error. The main results show that the tracking performance can be improved effectively by our control schemes. And it shows that our control schemes are more effective than traditional learning control approaches. Finally, the effectiveness is illustrated by numerical simulations.

在本文中，提出了不同类型的学习控制方案来研究二维离散神经网络的跟踪。学习控制方案结合了脉冲控制和迭代学习控制策略的优点，因为脉冲控制技术可以快速提高跟踪性能。此外，事件触发机制用于确定脉冲时间。通过构造一个触发函数，提出了一个等效系统，用于克服理论分析中的困难。然后根据等效系统设计了学习控制方案，并提出了一些充分的条件来保证跟踪误差的收敛。主要结果表明，通过我们的控制方案可以有效地提高跟踪性能。它表明我们的控制方案比传统的学习控制方法更有效。最后，通过数值模拟说明了有效性。