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基于混沌理论和改进径向基函数神经网络的网络舆情预测方法

魏德志 陈福集 郑小雪

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基于混沌理论和改进径向基函数神经网络的网络舆情预测方法

魏德志, 陈福集, 郑小雪

Internet public opinion chaotic prediction based on chaos theory and the improved radial basis function in neural networks

Wei De-Zhi, Chen Fu-Ji, Zheng Xiao-Xue
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  • 网络舆情发展趋势具有混沌系统的特征, 提出一种基于EMPSO-RBF神经网络的方法对网络舆情的发展趋势进行预测. 首先根据Lyapunov指数证明网络舆情具备混沌的特征, 然后对网络舆情时间序列数据进行相空间重构, 最后采用EMPSO-RBF方法进行预测, 并和其他模型进行对比试验, 实验结果表明EMPSO-RBF方法具有较高精确度.
    Information of internet public opinion is influenced by many netizens and net medias; characteristics of this information are non regular, stochastic, and may be expressed by a nonlinear complex evolution system. Corresponding model is difficult to establish and effectively predicted using the traditional methods based on statistical and machine learning. Characteristics of internet public opinion are chaotic, so the chaos theory can be introduced to research first, then the information of internet public opinion having chaotic characteristic is proved by the Lyapunov index. The model to predict the development trend of internet public opinion is next established by the phase space reconstruction theory. Finally, the hybrid algorithm EMPSO-RBF which is based on EM algorithm and the RBF neural network optimized by the improved PSO algorithm is proposed to solve the model. The hybrid algorithm fully takes the advantage of the EM clustering algorithm and the improved PSO, so the RBF neural network is improved by initializing the network structure in the early stage and optimizing the network parameters later. First, the EM clustering algorithm is used to obtain the center value and variance, and the radial basis function is improved with the combination of traditional Gauss model. Then the relevant network parameters are obtained by the improved PSO algorithm which is based on error optimizing the network parameters constantly. The model algorithm can be accurately simulated in the time series of chaotic information by experiments which are validated by different chaotic time series information; and it can better describe the development trend of different information of internet public opinion. The predicted results are made for government to monitor and guide the information of internet public opinion and benefit the social harmony and stability.
    • 基金项目: 国家自然科学基金(批准号:71271056)和福建省教育厅项目(批准号:C13001,JA14368)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 71271056), and the Department of Education of Fujian Province, China(Grant Nos. C13001, JA14368).
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    Henon M 1976 Commun. Math. Phys. 5 5069

  • [1]

    Bilal S, Ijaz M Q, Ihsanulhaq, Shafqatullah 2014 Chin. Phys. B 23 030502

    [2]

    Zhang Y 2013 Chin. Phys. B 22 050502

    [3]

    Zhou Y M, Li B C 2013 Journal of Data Acquisition & Processing. 28 69 (in Chinese) [周耀明, 李弼程 2013 数据采集与处理 28 69]

    [4]

    Liu J, Shi S T, Zhao J C 2013 Chin. Phys. B 22 010505

    [5]

    Sun Z H, Jiang F 2010 Chin. Phys. B 19 110502

    [6]

    Keerthi S S, Lin C J 2003 Neural Computation. 15 1667

    [7]

    Amjady N, Keynia F 2008 Int. J. Elec. Power. 30 533

    [8]

    Wu X D, Wang Y L, Liu W T, Zhu Z Y 2011 Chin. Phys. B 20 069201

    [9]

    Tang Z J, Ren F, Peng T, Wang W B 2014 Acta Phys. Sin. 63 050505 (in Chinese) [唐舟进, 任峰, 彭涛, 王文博 2014 物理学报 63 050505]

    [10]

    Song T, Li J 2012 Acta Phys. Sin. 61 080506 (in Chinese) [宋彤, 李菡 2012 物理学报 61 080506]

    [11]

    Zhang X Q, Liang J 2013 Acta Phys. Sin. 62 050505 (in Chinese) [张学清, 梁军 2013 物理学报 62 050505]

    [12]

    Zhao Y P, Zhang L Y, Li D C, Wang L F, Jiang H Z 2013 Acta Phys. Sin. 62 120511 (in Chinese) [赵永平, 张丽艳, 李德才, 王立峰, 蒋洪章 2013 物理学报 62 120511]

    [13]

    Xiao B X, Wang X W, Liu Y F 2007 Journal of System Simulation. 19 1382 (in Chinese) [肖本贤, 王晓伟, 刘一福 2007 系统仿真学报 19 1382]

    [14]

    Henon M 1976 Commun. Math. Phys. 5 5069

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出版历程
  • 收稿日期:  2014-09-12
  • 修回日期:  2014-10-27
  • 刊出日期:  2015-06-05

基于混沌理论和改进径向基函数神经网络的网络舆情预测方法

  • 1. 福州大学经济与管理学院, 福州 350108;
  • 2. 集美大学诚毅学院, 厦门 361021
    基金项目: 国家自然科学基金(批准号:71271056)和福建省教育厅项目(批准号:C13001,JA14368)资助的课题.

摘要: 网络舆情发展趋势具有混沌系统的特征, 提出一种基于EMPSO-RBF神经网络的方法对网络舆情的发展趋势进行预测. 首先根据Lyapunov指数证明网络舆情具备混沌的特征, 然后对网络舆情时间序列数据进行相空间重构, 最后采用EMPSO-RBF方法进行预测, 并和其他模型进行对比试验, 实验结果表明EMPSO-RBF方法具有较高精确度.

English Abstract

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