Fixed-Time Output-Constrained Synchronization of Unknown Chaotic Financial Systems Using Neural Learning

This article addresses the challenging problem of fixed-time output-constrained synchronization for master–slave chaotic financial systems with unknown parameters and perturbations. A neural adaptive control approach is originally proposed aid barrier Lyapunov function (BLF) network (NN) identification. The BLF introduced to preserve errors always within predefined output constraints. NN adopted identify compound item in error system. Unlike conventional identification, concept indirect identification employed, only a single learning parameter required be adjusted online. According stability argument, controller can ensure that all variables closed-loop system regulate minor residual sets around zero fixed time. Finally, simulations comparisons are conducted verify efficiency benefits strategy. It concluded from simulation results capable achieving better performance than compared linear feedback controller.