《计算机应用研究》|Application Research of Computers

基于改进的深度置信网络的电离层F2层临界频率预测

Ionosphere F2 layer critical frequency predict based on improved deep belief networks

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作者 唐智灵,吕晓朦
机构 桂林电子科技大学 a.无线宽带通信和信息处理重点实验室;b.电子工程与自动化学院,广西 桂林 541004
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文章编号 1001-3695(2018)03-0825-05
DOI 10.3969/j.issn.1001-3695.2018.03.038
摘要 提出一种基于深度置信网络(deep belief network,DBN)对本区域未来24 h的电离层临界频率f0F2预测的方法。对选取的数据集进行筛选,生成用于训练和测试的数据集;改进DBN基本单元的结构,以适应对连续型数据特征的提取与学习,再通过实验确定DBN的基本结构;最后利用训练数据集对改进后的网络进行训练,实现对 f0F2值的预测。与实测值相比较,改进的DBN具有极佳的预测准确性;与浅层结构BP网络和SVM网络相比,改进的DBN不单克服了浅层结构所固有的问题,更表现出对于连续型数据预测的优异性能,尤其是当预测对象受到高维复杂因素影响时改进的DBN模型依旧能表现出很好的预测性能。
关键词 f0F2预测;深度学习;深度置信网络;受限波尔兹曼机
基金项目 国家自然科学基金资助项目(61461013)
广西无线宽带通信与信号处理重点实验室基金资助项目(GXKL06160103)
桂林电子科技大学创新团队基金资助项目
本文URL http://www.arocmag.com/article/01-2018-03-038.html
英文标题 Ionosphere F2 layer critical frequency predict based on improved deep belief networks
作者英文名 Tang Zhiling, Lyu Xiaomeng
机构英文名 a.KeyLaboratoryofWirelessBroadbandCommunications&InformationProcessing,b.InstituteofElectricalEngineering&Automation,GuilinUniversityofElectronicTechnology,GuilinGuangxi541004,China
英文摘要 This paper proposed a method which was predicting the ionospheric critical frequency f0F2 of the future 24h based on deep belief network(DBN). First, it filtered the data and processed into data sets for training and testing. Secondly, it improved the structure of the basic unit of DBN to adapt to the extraction and learning of continuous data feature, and then determined the basic structure of DBN through experiments. Finally, this paper used the training data set to train the improved network to realize the prediction of f0F2 value. Compared with the measured values, the improved DBN has excellent prediction accuracy. Compared with the shallow structure of BP network and SVM network, the improved DBN not only overcomes the inherent problems of the shallow structure, but also shows the excellent performance of continuous data prediction, especially when the prediction value is affected by high dimensional complex factors, the improved DBN model can still show good prediction performance.
英文关键词 f0F2 prediction; deep learning; deep belief network; restricted Boltzmann machine
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收稿日期 2016/11/15
修回日期 2017/1/13
页码 825-829
中图分类号 TP183
文献标志码 A