关键词: 比例UKF, 神经网络, Mackey-Grass, 预测

Abstract: One algorithm based on the Scaled Unscented Kalman Filter（Scaled-UKF） is proposed to estimate the weights of the neural network，which can overcome the BP algorithm’s weaknesses of slow learning speed，large computational complexity，and easy convergence to the local minimum points.Taking the Mackey-Grass chaos time sequences as its input，the neural network is simulated with the Scaled-UKF，UKF and BP algorithm.The result of the simulation indicates that the Scaled-UKF algorithm has the faster training speed and higher forecast precision than the BP algorithm，and may avoid the network’s convergence to the local minimum points.Comparing with the UKF algorithm，the Scaled-UKF algorithm can guarantee positive semi-definiteness of the state covariance and its variable distribution may not be Gaussian-distributed.

Key words: Scaled Unscented Kalman Filter（Scaled-UKF）, neural network, Mackey-Grass, forecast