Quasi-Synchronization and Quasi-Uniform Synchronization of Caputo Fractional Variable-Parameter Neural Networks with Probabilistic Time-Varying Delays

Owing to the symmetry between drive–response systems, discussions of synchronization performance are greatly significant while exploring dynamics neural network systems. This paper investigates quasi-synchronization (QS) and quasi-uniform (QUS) issues systems on fractional-order variable-parameter networks (VPNNs) including probabilistic time-varying delays. The effects system parameters, probability distributions order QS QUS considered. By applying Lyapunov–Krasovskii functional approach, Hölder’s inequality Jensen’s inequality, criteria VPNNs under controller designs with constant gain coefficients derived. obtained related Caputo derivative, which can avoid situation in upper bound an interval time delay is too large yet occurrence very small, information such as size fully Finally, two examples presented further confirm effectiveness algebraic different distributions.