Uncertainty meets fixed-time control in neural networks

The fixed-time stability on uncertain neural networks with adaptive coupling strength is addressed in this paper. To beign with, a new time-varying coupling strength is introduced, which is based on the projective transform of quantized error systems. Next, to address various disturbances in master-slave systems, inhomogeneous uncertainty is taken into consideration. Then, a multi-quantized fixed-time controller is put forward to ensure that the systems reach to a prescribed trajectory in the settling time. Afterward, sufficient synchronization criteria are derived on the strength of the Lyapunov stability theorem. Finally, simulation examples are given to validate the theoretical analysis.