Adaptive synchronization of delayed reaction-diffusion neural networks with unknown non-identical time-varying coupling strengths

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.