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dc.contributor.authorTripathi, K. C.-
dc.contributor.authorDas, I. M. L.-
dc.description.abstractThe artificial neural network (ANN) has been used for simulation of Antarctic sea ice area anomalies. Various dominant cycles present in the data have been identified using the Fourier analysis. It has been found that the data of the Antarctic sea ice area has two dominant cycles: annual and half yearly. The effect of the presence and / or absence of these dominant cycles on the simulation results have been carried out. ANN can simulate the broad trend of the sea ice area anomalies when all the cycles are present. However, the prediction skill of model for intraseasonal variability degrades as we remove the trends. Further, the forecast have been verified on the basis of various attributes of the forecast.en_US
dc.sourceIJMS Vol.37(1) [March 2008]en_US
dc.subjectArtificial neural networken_US
dc.subjectAntarctic sea iceen_US
dc.subjectFourier analysisen_US
dc.subjectActivation functionen_US
dc.subjectSea iceen_US
dc.subjectError back propagationen_US
dc.titleSimulation of Antarctic sea ice area with artificial neural networken_US
Appears in Collections:IJMS Vol.37(1) [March 2008]

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