国家自然科学基金(BK2009727)

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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被引量：2
《Chinese Physics B》2011年第6期546-551,共6页伍雪冬 王耀南 刘维亭 朱志宇 
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);the National Natural Science Foundation of China (Grant No. 61075028)
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 in...
关键词：prediction of time series with missing data random interruption failures in the observation neural network approximation 
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