WNN Algorithm Based on Improved Unscented Kalman Filter and Its Applications
Gao Hai-long · Journal of Kunming University of Science and Technology · 2012
Unscented Kalman Filter(UKF),which is a combination of Unscented Transform(UT) and standard Kalman filter,has a good estimation performance to the nonlinear system.The parameters of Wavelet Neural Network(WNN) by UKF do not need to calculate the derivative of Jacobian matrix with fast speed and high accuracy.But UKF is computationally expensive.Based on this,an improved UKF is introduced into the parameters estimation of WNN to raise the training efficiency.The improved UKF adopts an UT based on minimal skew simplex Sigma point sampling strategy in the system of Kalman filter which has the merits of UKF,and improves the computational efficiency greatly.Simulation results show that WNN based on the improved UKF has faster training speed and higher accuracy than that of EKF,and has an approximately close accuracy to that of UKF but with high computation efficiency.