Local Stabilization of Delayed Fractional-Order Neural Networks Subject to Actuator Saturation

This paper investigates the local stabilization problem of delayed fractional-order neural networks (FNNs) under influence actuator saturation. First, sector condition and dead-zone nonlinear function are specially introduced to characterize features saturation phenomenon. Then, based on Lyapunov method estimation technique Mittag–Leffler function, an LMIs-based criterion is derived guarantee stability closed-loop FNNs subject Furthermore, two corresponding convex optimization schemes proposed minimize costs expand region admissible initial values, respectively. At last, simulation examples developed demonstrate feasibility effectiveness results.