Apply BPNN with Kalman Filtering to the dynamic system identification

System identification is an important area in control system. In this paper we discuss some of the reasons caused the slow convergence for BPNN and the effect of the number of neurons in the hidden layer when apply BPNN with Kalman Filtering to dynamic system identification. BPNN is base on LMS, and uses steepest descent method to find the optimum weighting connects to the adjacent layer. It always consumes much of time while training, and not easy to get a global optimal value while applying to on-line training. Kalman Filtering is a better linear and discrete method for parameters estimation. By this way to solve a problem, it can involve the initial conditions, and also can apply to stationary and non-stationary system. So, applying BPNN and Kalman Filtering together to the dynamic system identification, it will get a satisfactory result both on convergent efficiency and stable.