Inverse Model Identification of Nonlinear Dynamic System using Neural Network

This paper investigates the inverse identification of the dynamics nonlinear plants using improved backpropagation (BPNN) neural network. The structure and algorithm of inverse model identification, which is based on improved BPNN, are presented. Essential point of the proposed approach is to make use of the direct inverse learning scheme to achieve simple and accurate inverse system identification. This approach can easily be extended to the area of on-line adaptive control. Simulation results show that the proposed method is efficacious used to identify nonlinear dynamic system, inverse models can be satisfactorily achieved, and the accuracy, the response speed and static error can be evidently improved.