Neural Network Forecast Algorithm Based on Iterated Unscented Kalman Filter

DOI

10.2991/iccasm.2012.313How to use a DOI?

Keywords

Forecast, Iterated unscented Kalman filter, Neural network

Abstract

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, UKF and 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 network’s convergence getting into the local minimum points. Compared with UKF algorithm, the proposed algorithm has a higher forecast precision.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiangheng Liu
AU  - Guobin Chang
AU  - Baiqing Hu
PY  - 2012/08
DA  - 2012/08
TI  - Neural Network Forecast Algorithm Based on Iterated Unscented Kalman Filter
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1230
EP  - 1233
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.313
DO  - 10.2991/iccasm.2012.313
ID  - Liu2012/08
ER  -