Neural Network Forecast Algorithm Based on Iterated Unscented Kalman Filter

摘要：

　　A novel algorithm based on the iterated unscented Kalman filter (IUKF) is proposed in this paper to train the weights and bias of the neural network.In the proposed algorithm,the weights and bias are considered as the states,and the outputs of the network are used as the measurements for the IUKF.In IUKF,the iteration concept is introduced into the unscented Kalman filter (UKF).By substituting the updated mean and covariance into the unscented transformation (UT),the total forecast precision is improved.Taking the Mackey-Glass chaos time sequences as an input of the net,the neural network is simulated with the IUKF,UKFand back propagation (BP) algorithms.The simulation results indicate that the IUKF algorithm has a faster training speed and higher forecast precision than the BP algorithm.Moreover,the IUKF algorithm avoids the uetworks convergence getting into the local minimum points.Compared with UKF algorithm,the proposed algorithm has a higher forecast precision.

关键词： Forecast Iterated unscented Kalman filter Neural network

作者: Liu Xiangheng Chang Guobin Hu Baiqing

作者单位: Department of navigation engineering Naval University of Engineering Wuhan, China, 430033

会议类型: 国际会议

会议名称: 2012 2nd International Conference on Computer Application and System Modeling(2012第二届计算机应用与系统建模国际会议)(ICCASM-2012)

会议地点: 沈阳

会议语种:英文

页码: 1230-1233

在线出版日期: 2012-07-27（万方平台首次上网日期，不代表论文的发表时间）