Abstract: A pseudo-impedance learning algorithm is discussed to overcome the drawbacks of the slow convergence speed and the local minimum of the back-propagation algorithm.In the meantime,by utilizing the learning capacity and nonlinear characteristic of neural networks,a method of state estimation is discussed for nonlinear dynamic systems. Simulation shows that the learning algorithm has a fast convergence speed and good stability.The ability of following the tracks of states can be obtained when the algorithm is applied to state estimation.