Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

doi:10.1088/1674-1056/20/6/069201

中国物理B ›› 2011, Vol. 20 ›› Issue (6): 69201-069201.doi: 10.1088/1674-1056/20/6/069201

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Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

王耀南¹, 伍雪冬², 刘维亭², 朱志宇²

(1)College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; (2)School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China

收稿日期:2010-10-05 修回日期:2011-01-19 出版日期:2011-06-15 发布日期:2011-06-15
基金资助:
Project supported by the State Key Program of the National Natural Science of China (Grant No. 60835004), the Natural Science Foundation of Jiangsu Province of China (Grant No. BK2009727), the Natural Science Foundation of Higher Education Institutions of Jiangsu Province of China (Grant No. 10KJB510004), and the National Natural Science Foundation of China (Grant No. 61075028).

Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

Wu Xue-Dong(伍雪冬)^a)†, Wang Yao-Nan(王耀南)^b), Liu Wei-Ting(刘维亭)^a), and Zhu Zhi-Yu(朱志宇)^a)

^a School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China; ^b College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Received:2010-10-05 Revised:2011-01-19 Online:2011-06-15 Published:2011-06-15
Supported by:
Project supported by the State Key Program of the National Natural Science of China (Grant No. 60835004), the Natural Science Foundation of Jiangsu Province of China (Grant No. BK2009727), the Natural Science Foundation of Higher Education Institutions of Jiangsu Province of China (Grant No. 10KJB510004), and the National Natural Science Foundation of China (Grant No. 61075028).

摘要/Abstract

摘要： On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.

关键词: prediction of time series with missing data, random interruption failures in the observation, neural network approximation

Abstract: On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.

Key words: prediction of time series with missing data, random interruption failures in the observation, neural network approximation

中图分类号: (Radioactivity and radioisotopes)

92.20.Td

05.45.Tp (Time series analysis) 05.10.Gg (Stochastic analysis methods)

伍雪冬, 王耀南, 刘维亭, 朱志宇. Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data[J]. 中国物理B, 2011, 20(6): 69201-069201.

Wu Xue-Dong(伍雪冬), Wang Yao-Nan(王耀南), Liu Wei-Ting(刘维亭), and Zhu Zhi-Yu(朱志宇). Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data[J]. Chin. Phys. B, 2011, 20(6): 69201-069201.

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Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

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