Memory synchronisation of delayed neural networks via variable sampling control

Abstract

This paper tackles the challenge of memory synchronisation of delayed neural networks by implementing a variable sampling control approach. On the one hand, in the light of constructing novel Lyapunov functionals, the conservativeness of the criteria obtained for delayed neural networks is significantly reduced. On the other hand, in light of memory sampling method, the transmission delay is introduced in sampling control scheme, and the information can be fully utilised. The constructed Lyapunov functionals have an advantage, i.e. it is not necessary to be positive on sampling intervals, and also to be continuous at the sampling instants. Finally, to demonstrate the effectiveness and advantages of the proposed methods, two experimental simulations are conducted for delayed neural networks. Two simulations provide empirical evidence supporting the validity of the techniques developed in this study, highlighting their potential for practical application in delayed neural network systems.