Outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches

Abstract This paper is committed to investigating outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches. Considering the low cost and high reliability of data-sampling control, we adopt two categories of control strategies with principles of centralized and decentralized data-sampling to synchronize fractional-order neural networks with deviating argument. Several sufficient criteria are proposed to realize outer-synchronization by data-sampling control design in two complex coupled networks. It is noteworthy that, based on centralized and decentralized data-sampling methods, the synchronization theory of fractional systems and differential equation with deviating argument, the sampling time points are very well selected in control systems. An example is performed to illustrate the advantage of the presented theoretical analysis and results.