Abstract
This paper presents a method for achieving synchronization of chaotic systems with unknown dynamics, using a predefined-time neural learning control approach. The proposed method includes a control law for synchronization and a parameter updating law that are designed to ensure stability according to the predefined-time Lyapunov theory. The analysis of stability indicates that the synchronization errors using this approach converge to a small region around zero within the predefined time. The effectiveness of the proposed method is demonstrated through simulation examples.
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