ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (8): 1736-1743.doi: 10.7544/issn1000-1239.2017.20170213

所属专题: 2017人工智能前沿进展专题

• 人工智能 • 上一篇    下一篇

基于Storm的在线序列极限学习机的气象预测模型

欧阳建权1,2,周勇1,唐欢容1,2   

  1. 1(湘潭大学信息工程学院 湖南湘潭 411105);2(智能计算与信息处理教育部重点实验室(湘潭大学) 湖南湘潭 411105) (oyjq@xtu.edu.cn)
  • 出版日期: 2017-08-01
  • 基金资助: 
    国家自然科学基金项目(61672495);湖南省教育厅重点项目(16A208)

A Meteorological Predication Model Based on Storm and Online Sequential Extreme Learning Machine

Ouyang Jianquan1,2, Zhou Yong1, Tang Huanrong1,2   

  1. 1(College of Information Engineering, Xiangtan University, Xiangtan, Hunan 411105);2(Key Laboratory of Intelligence Computing and Information Processing(Xiangtan University), Ministry of Education, Xiangtan, Hunan 411105)
  • Online: 2017-08-01

摘要: 为提高气象预测精度,实时应对频发的局域气象灾害,拥有更高的处理海量数据的效率,提出了一种基于Storm的在线序列的极限学习机气象预测模型.该模型首先初始化多个在线极限学习机,当新批次的数据不断到达时,模型能够在训练结果的基础上继续学习新样本,并引入随机梯度下降法和误差权值调整方法,对新的预测结果进行误差反馈,实时更新误差权值参数,以提高模型预测准确率.另外,采用Storm流式处理框架对提出的算法模型进行并行化改进,以提高处理海量高维数据的能力.实验结果表明:该模型与基于Hadoop的并行极限学习机算法(parallel extreme learning machine, PELM)相比,具有更高的预测精度和优异的并行性能.

关键词: Storm, 极限学习机, 气象预测, 在线序列, 机器学习

Abstract: In order to improve the accuracy of meteorological forecasting, deal with frequent local meteorological disasters in real time, and have higher efficiency of dealing with massive data, this paper proposes a meteorological forecasting model using the Storm-based online sequential extreme learning machine. The model firstly initializes multiple online extreme learning machine. When new batches of data arrive, the model continually studies the new data samples based on the training results, and introduces the stochastic gradient descent method and the error weight adjustment method to give the error feedback for new prediction results and then update the error weight parameters in real time, and finally to improve prediction accuracy. In addition, the Storm flow processing framework is adopted to improve the proposed model in the aspect of parallelism in order to enhance the ability of dealing with massive high-dimensional data. The experimental results show that compared with the Hadoop-based parallel extreme learning machine (PELM), the proposed model has higher prediction accuracy and more excellent parallelism.

Key words: Storm, extreme learning machine, meteorological predication, online sequence, machine learning

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