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Zaporozhtsev I.F., Sereda A.-V.I.
Artificial neural networks application to temporal variability forecast of ocean surface characteristics spatial distribution
Abstract. Ocean surface characteristics maps forecasting methodology has been suggested and implemented in a series of numerical experiments (with altimetry samples). Maps dataset has been considered to be a multivariate time series constructed with regularly gridded values. The methodology is based on the idea of multilayer perceptron application to the forecasting problem.
Keywords: multivariate time series forecasting, clusterization, artificial neural networks, sea level anomalies
Printed reference: Zaporozhtsev I.F., Sereda A.-V.I. Artificial neural networks application to temporal variability forecast of ocean surface characteristics spatial distribution // Vestnik of MSTU. 2013. V. 16, No 4. P. 708-714.
Electronic reference: Zaporozhtsev I.F., Sereda A.-V.I. Artificial neural networks application to temporal variability forecast of ocean surface characteristics spatial distribution // Vestnik of MSTU. 2013. V. 16, No 4. P. 708-714. URL: http://vestnik.mstu.edu.ru/v16_4_n54/708_714_zaporo.pdf.
