| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 6864623 | Neurocomputing | 2018 | 33 Pages |
Abstract
This paper investigates an adaptive neural network prescribed performance synchronization scheme for unknown complex dynamic networks with different dimensions. Based on predefined performance bounded and Lyapunov stability theory, adaptive neural networks controllers are designed to ensure that synchronization errors remain in a neighborhood of origin with the prescribed bounds. In addition, the paper analyses in detail that the synchronization behaviors between drive network selected as the three-dimension chaotic system and response network selected as the four-dimension hyperchaotic chaotic system. The numerical simulation results are presented to show the validity of the proposed approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Aili Fan, Junmin Li,