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Prediction of horizontal component of earth's magnetic field over Indian sector using neural network model

机译:基于神经网络模型的印度地区地磁场水平分量预测

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摘要

Present work is the first attempt to predict horizontal component of earth's magnetic field (H) and range in H (Delta H) over Indian sector by considering the stations, namely, Trivandrum, Pondicherry, Visakhapatnam, and Nagpur, using the concept of neural network (NN). Through training procedure, solar flux (F10.7), latitude, longitude, day of the year, local time, Ap index, IMF Bz, and ion number density are identified as the optimum choice of input parameters, whereas the inclusion of solar wind pressure and velocity has not significantly improved the performance of the model. Thus an appropriate neural network model, NSSHC has been developed with 12 hidden neurons and 500 iterations to predict H component and range in H (Delta H) during the period 1996-2001, to capture diurnal, seasonal, latitudinal, magnetic and solar activity effects. (C) 2014 Elsevier Ltd. All rights reserved.
机译:当前的工作是首次尝试通过使用神经网络的概念,通过考虑台站Trivandrum,Pondicherry,Visakhapatnam和Nagpur来预测印度区域上地球磁场(H)的水平分量和H的范围(Delta H)。 (NN)。通过训练程序,太阳通量(F10.7),纬度,经度,一年中的某天,当地时间,Ap指数,IMF Bz和离子数密度被确定为输入参数的最佳选择,而包含太阳风压力和速度并未显着改善模型的性能。因此,已经开发了一种合适的神经网络模型NSSHC,它具有12个隐藏的神经元和500次迭代,以预测1996-2001年期间H的H分量和H的范围(Delta H),以捕获昼夜,季节性,纬度,磁和太阳活动效应。 (C)2014 Elsevier Ltd.保留所有权利。

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