The near-optimal control scheme is proposed for a class of unknown continuous-time nonlinear systems based
on single network approximate dynamic programming (ADP). The proposed scheme relaxes the requirement of the system
model being known or partly known by designing a novel recurrent neural network(RNN) identifier, and eliminates the action
network of ordinary ADP methods by employing a neural network(NN) to approximate the performance index function. By
Lyapunov theory, it is proved that all the signals in the closed-loop system are ultimately uniformly bounded and the obtained
optimal performance index function and control input lie in small neighborhoods of the optimal performance index function
and the optimal control input, respectively. Simulation results demonstrate the effectiveness of the proposed scheme.