Abstract In this paper, we proposed an iterative learning control (ILC) update rule to synchronize an array of non-identical time-varying delayed neural network systems in a repetitive environment. Under the identical initial conditions, we employed a distributed D-type ILC update rule that guaranteed synchronization by choosing the appropriate inner coupling matrix. Besides, to accommodate non-identical initial conditions, we proposed another adaptive ILC update rule that also could synchronize the systems. Two numerical simulations are presented to illustrate the effectiveness of the theoretical results.

中文翻译：

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通过迭代学习控制同步非相同时变延迟神经网络系统

摘要 在本文中，我们提出了一种迭代学习控制 (ILC) 更新规则，以在重复环境中同步一组不同的时变延迟神经网络系统。在相同的初始条件下，我们采用分布式 D 型 ILC 更新规则，通过选择适当的内部耦合矩阵来保证同步。此外，为了适应不同的初始条件，我们提出了另一种自适应 ILC 更新规则，它也可以同步系统。给出了两个数值模拟来说明理论结果的有效性。