This paper has proposed an adaptive control scheme based on a hybrid neural network (HNN) to address the problem of uncertain nonlinear systems with discrete-time having bounded disturbances. This proposed control scheme is composed of a neural network (NN) and differential evolution (DE) technique which is used to initialize the weights of the NN and the controller is designed in such a manner so that the stability can be ensured and the desired trajectory can be achieved. The designed HNN is employed to approximate unknown functions present in the system. By using the concept of system transformation, the adaptive law and controller are designed and the whole system is proved to be stable in the sense of semi-globally uniformly ultimately boundedness (SGUUB) with the assistance of Lyapunov theory. Finally, the validity and effectiveness of the results are proved through two simulation examples.