Investigation of different topologies of neural networks for data assimilation
Автор: Hrter Fabrcio Pereira, Campos Velho Haroldo Fraga
Рубрика: Дискретная математика и математическая кибернетика
Статья в выпуске: 4 т.3, 2014 года.
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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. The best results of the MP-NN for sampled observation at 0.06 and 0.01 s were obtained using 3 neurons, with hyperbolic-tangent in the output layer. For RBF-NN, the best
Data assimilation, neural network
Короткий адрес: https://sciup.org/147160551
IDR: 147160551 | DOI: 10.14529/cmse140407
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