Synchronizing non-identical time-varying delayed neural network systems via iterative learning control

In this paper, we proposed an iterative learning control (ILC) update rule to synchronize an array of nonidentical 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. (c) 2020 Elsevier B.V. All rights reserved.