Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4948227 | Neurocomputing | 2017 | 10 Pages |
Abstract
This paper concerns the asymptotic synchronization of delayed reaction-diffusion neural networks (RDNNs) with unknown nonidentical time-varying coupling strengths, where the time-varying coupling strengths are consist of continuous time-varying periodic parameters and time-invariant nonnegative parameters. By utilizing a novel adaptive approach, the differential-difference type adaptive laws of coupling strengths and adaptive controller are designed such that the nonidentical RDNNs are asymptotic synchronization. The sufficient conditions dependent on the reaction-diffusion terms are derived by constructing a novel Lyapunov-Krasovskii-like composite energy functional (CEF) and using Barbalat's lemma. Finally, a simulation example is provided to illustrate the effectiveness of the developed approach.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Junmin Li, Chao He, Weiyuan Zhang, Minglai Chen,