A Hybrid Extended Kalman Filter Based on a Parametrized FeedForward Neural Network for the Improvement of the Results of Numerical Wave Prediction Models-Reference-Cited by-同舟云学术

A Hybrid Extended Kalman Filter Based on a Parametrized FeedForward Neural Network for the Improvement of the Results of Numerical Wave Prediction Models

Published:2023-09-20 Issue: Volume: Page:
ISSN:
Container-title:16th International Conference on Meteorology, Climatology and Atmospheric Physics—COMECAP 2023
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Author:

Donas Athanasios¹,Galanis George²,Famelis Ioannis Th.¹^ORCID

Affiliation:

1. microSENSES Laboratory, Department of Electrical and Electronics Engineering, Ancient Olive Grove Campus, University of West Attica, 12241 Athens, Greece

2. Mathematical Modeling and Applications Laboratory, Hellenic Naval Academy, Hatzikiriakion, 18539 Piraeus, Greece

Publisher

MDPI

Link

https://www.mdpi.com/2673-4931/26/1/199/pdf

Reference8 articles.

1. Runge-Kutta solutions for an environmental parameter prediction boundary value problem;Famelis;J. Coupled Syst. Mul-tiscale Dyn. Exp.,2014

2. Classical and Quasi-Newton Methods for a Meteorological Parameters Prediction Boundary Value Problem;Famelis;Appl. Math. Inf. Sci.,2014

3. Quadric Shooting Solution for an Environmental parameter prediction model;Famelis;Far East J. Appl. Math.,2015

4. Statistical post processes for the improvement of the results of numerical wave prediction models. A combination of Kolmogorov-Zurbenko and Kalman filters;Galanis;J. Oper. Oceanogr.,2011

5. Haykin, S. (2001). Kalman Filtering And Neural Networks, John Wiley & Sons.

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