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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于误差分布补偿的混杂模糊神经网络时间序列预测模型

安杰 王梦灵

安杰, 王梦灵. 基于误差分布补偿的混杂模糊神经网络时间序列预测模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20200212001
引用本文: 安杰, 王梦灵. 基于误差分布补偿的混杂模糊神经网络时间序列预测模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20200212001
AN Jie, WANG Mengling. Hybrid Fuzzy Neural Network based on Error Distribution Analysis for Time-series Prediction[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20200212001
Citation: AN Jie, WANG Mengling. Hybrid Fuzzy Neural Network based on Error Distribution Analysis for Time-series Prediction[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20200212001

基于误差分布补偿的混杂模糊神经网络时间序列预测模型

doi: 10.14135/j.cnki.1006-3080.20200212001
基金项目: 上海市“科技创新计划”人工智能专项(19DZ1209003)
详细信息
    作者简介:

    安杰:安 杰(1996—),男,安徽芜湖人,硕士生,主要研究领域为交通流预测、交通大数据挖掘。E-mail:anan6727@qq.com

    通讯作者:

    王梦灵,E-mail:wml_ling@ecust.edu.cn

  • 中图分类号: TP391.9

Hybrid Fuzzy Neural Network based on Error Distribution Analysis for Time-series Prediction

  • 摘要: 针对时间序列预测问题,提出了一种基于误差分布分析的混杂模糊神经网络预测模型。首先提出了一种混杂模糊神经网络结构,将原单一输出层替换为由一个全连接层和非线性激活函数混合的组合网络,用于学习组合隶属度层的输出。然后,提出了基于误差分布的损失函数,使得更新参数的过程中既考虑了误差的大小又考虑了期望的误差分布。根据新的模型结构和新的损失函数,梯度下降过程中,预测误差小而出现概率较高或误差大而出现率低的两类样本将获得较少的训练梯度贡献,而处于中间的样本在训练过程中获得更新增益,通过证明表明本文提出的方法可以获得更均匀和稳定的预测输出。最后通过两个仿真实验验证了本文提出的方法的有效性和准确性。

     

  • 图  1  五层T-S 模糊神经网络

    Figure  1.  The five-layer-T-S FNN

    图  2  提出的混杂模糊神经网络

    Figure  2.  The proposed HFNN

    图  3  误差梯度概率密度和正态分布图

    Figure  3.  The density of B(d), A(d) and N(d).

    图  4  非线性系统建模误差分布对比

    Figure  4.  HFNN, LSTM errors distribution for nonlinear system modeling

    图  5  误差坐标系中的误差分布

    Figure  5.  Error zone analysis in statistical error coordinate.

    图  6  部分交通预测与真实值

    Figure  6.  Traffic forecast and true value

    图  7  交通流量预测误差的分布

    Figure  7.  Traffic forecast error zone analysis in statistical error coordinate.

    表  1  FNN, LSTM和HFNN等非线性系统建模实验结果

    Table  1.   Experimental results off FNN, LSTM and HFNN for nonlinear system modeling

    MSE
    meanvar
    HFNN1.5870×10−52.5497×10−5
    FNN3.3466×10−55.7975×10−5
    LSTM2.6356×10−53.1110×10−5
    BPNN2.8839×10−53.2695×10−5
    ARIMA2.8840×10−56.6095×10−5
    DBN+FNN[16]2.8839×10−52.6355×10−5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-12
  • 网络出版日期:  2022-05-13

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