GEKF, GUKF and GGPF based prediction of chaotic time-series with additive and multiplicative noises |
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Authors: | Wu Xue-Dong and Song Zhi-Huan |
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Institution: | State Key Laboratory of Industrial Control Technology,
Department of Control Science and Engineering,
Zhejiang
University, Hangzhou
310027, China |
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Abstract: | On the assumption that random interruptions in the observation
process are modelled by a sequence of independent Bernoulli random
variables, this paper generalize the extended Kalman filtering
(EKF), the unscented Kalman filtering (UKF) and the Gaussian
particle filtering (GPF) to the case in which there is a positive
probability that the observation in each time consists of noise
alone and does not contain the chaotic signal (These generalized
novel algorithms are referred to as GEKF, GUKF and GGPF
correspondingly in this paper). Using weights and network output of
neural networks to constitute state equation and observation
equation for chaotic time-series prediction to obtain the linear
system state transition equation with continuous update scheme in an
online fashion, and the prediction results of chaotic time series
represented by the predicted observation value, these proposed novel
algorithms are applied to the prediction of Mackey--Glass time-series
with additive and multiplicative noises. Simulation results prove
that the GGPF provides a relatively better prediction performance in
comparison with GEKF and GUKF. |
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Keywords: | additive and multiplicative
noises different generalized nonlinear filtering chaotic
time-series prediction neural network approximation |
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