Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data-Reference-Cited by-同舟云学术

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

Published:2011-06 Issue:6 Volume:20 Page:069201
ISSN:1674-1056
Container-title:Chinese Physics B
language:
Short-container-title:Chinese Phys. B

Author:

Wu Xue-Dong,Wang Yao-Nan,Liu Wei-Ting,Zhu Zhi-Yu

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference22 articles.

1. Natural radioactivity levels in soil samples from some areas of Himachal Pradesh, India using γ-ray spectrometry

2. Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm

3. Multi-step ahead nonlinear identification of Lorenz’s chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

4. BP neural network with rough set for short term load forecasting

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