Abstract: A one-step-ahead predictive control algorithm via neural network identification is proposed for the control of nonlinear systems.The algorithm eliminates the defect that neural networks are prone to be trapped in local minimum through utilizing chaos mechanism based on self-recurrent wavelet neural networks.Then adaptive learning ratio is adopted to enhance convergence ability and speed of neural networks.As the neural network being predictive model and the output feedback and deviation rectification being introduced to reduce predictive error,a one-step-ahead weighted predictive control performance index is formulated.Lastly,the control law is derived via Brent optimization method which is efficient and reliable in one dimension search without knowing any relative derivative information.The method has less parameters to choose,which is very suitable for real-time control.The simulation shows that the presented method is effective.