Geodesic
Investigation of different topologies of neural networks for data assimilation
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 3 (2014) no. 4, pp. 96-108

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Neural networks have emerged as a novel scheme for a data assimilation process. Neural network techniques are applied for data assimilation in the Lorenz chaotic system. A radial basis function and a multilayer perceptron neural networks are trained employing 1000, 2000, and 4000 examples. Three different observation intervals are used: 0.01, 0.06 and 0.1 s. The performance of the data assimilation technique is investigated for different architectures of these neural networks.
Keywords: Neural Network
Mots-clés : data assimilation, Data Assimilation. 
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     author = {F. P. H\"arter and H. F. Campos Velho},
     title = {Investigation of different topologies of neural networks for data assimilation},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
     pages = {96--108},
     publisher = {mathdoc},
     volume = {3},
     number = {4},
     year = {2014},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/VYURV_2014_3_4_a6/}
}
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F. P. Härter; H. F. Campos Velho. Investigation of different topologies of neural networks for data assimilation. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 3 (2014) no. 4, pp. 96-108. http://geodesic.mathdoc.fr/item/VYURV_2014_3_4_a6/