| 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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| 作者姓名： | 伍雪冬 王耀南 刘维亭 朱志宇 |
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| 作者单位： | School of Electronics and Information, Jiangsu University of Science and Technology College of Electrical and Information Engineering, Hunan University |
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| 基金项目： | 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). |
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| 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.
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| 关 键 词： | prediction of time series with missing data random interruption failures in the observa- tion neural network approximation |
| 收稿时间： | 2010-10-05 |
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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| Wu Xue-Dong,Wang Yao-Nan,Liu Wei-Ting and Zhu Zhi-Yu.Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data[J].Chinese Physics B,2011,20(6):69201-069201. |
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| Authors: | Wu Xue-Dong Wang Yao-Nan Liu Wei-Ting and Zhu Zhi-Yu |
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| Institution: | School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China;School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China |
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| 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. |
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| Keywords: | prediction of time series with missing data random interruption failures in the observation neural network approximation |
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