Synchronizing Non-identical Time-varying Delayed Neural Network Systems via Iterative Learning Control

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.