Synchronization of delayed reactiondiffusion neural networks via an adaptive learning control approach
An adaptive learning control strategy is utilized to investigate the synchronization problem for delayed reactiondiffusion neural networks (RDNNs) with unknown time-varying coupling strengths. A novel adaptive synchronization approach is proposed, which is consisted of differentialdifference type updating law and feedback control law. By constructing a LyapunovKrasovskii-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.
synchronization delay reactiondiffusion neural networks unknown time-varying coupling strengths adaptive learning laws