Extended and unscented Kalman filtering based feedforward neural networks for time series prediction

DOI10.1016/J.APM.2011.07.052zbMath1243.93115OpenAlexW2050878373MaRDI QIDQ437862

Xuedong Wu, Yaonan Wang

Publication date: 20 July 2012

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2011.07.052

zbMATH Keywords

feedforward neural networktime series predictionnonlinear filtering approaches

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Filtering in stochastic control theory (93E11) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (4)

Multi-step prediction of time series with random missing data ⋮ A marginalized unscented Kalman filter for efficient parameter estimation with applications to finite element models ⋮ Filtering based multi-innovation extended stochastic gradient algorithm for Hammerstein nonlinear system modeling ⋮ Multiple sparse-grid Gauss-Hermite filtering

Cites Work

• Unnamed Item
• Kalman filtering with real-time applications
• Training radial basis neural networks with the extended Kalman filter
• A probabilistic method for assisting knowledge extraction from artificial neural networks used for hydrological prediction
• Oscillation and Chaos in Physiological Control Systems

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