Abstract
In this paper we apply a Neural Network (NN) to distill massive oceanographic datasets down to a new space of smaller dimension, thus characterizing the essential information contained in the data. Due to the natural nonlinearity of those data, traditional multivariate analysis may not represent reality. This work presents the methodology associated with the use of a multi-layer NN with a bottleneck to extract nonlinear information of the data.
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Botelho, S.S.C., Mata, M.M., de Bem, R., Almeida, I. (2003). Applying Neural Networks to Study the Mesoscale Variability of Oceanic Boundary Currents. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_100
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DOI: https://doi.org/10.1007/978-3-540-39592-8_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20256-1
Online ISBN: 978-3-540-39592-8
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