Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
468169 | Computers & Mathematics with Applications | 2013 | 11 Pages |
Abstract
An adaptive learning control strategy is utilized to investigate the synchronization problem for delayed reaction–diffusion neural networks (RDNNs) with unknown time-varying coupling strengths. A novel adaptive synchronization approach is proposed, which is consisted of differential–difference type updating law and feedback control law. By constructing a Lyapunov–Krasovskii-like composite energy functional (CEF), based on the LaSalle invariant principle of functional differential equations, a sufficient condition for the adaptive synchronization of such a system is obtained. Finally, a numerical example is given to show the effectiveness of the proposed synchronization method.
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Junmin Li, Weiyuan Zhang, Minglai Chen,