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多元混沌时间序列的因子回声状态网络预测模型

许美玲 韩敏

许美玲, 韩敏. 多元混沌时间序列的因子回声状态网络预测模型. 自动化学报, 2015, 41(5): 1042-1046. doi: 10.16383/j.aas.2015.c140604
引用本文: 许美玲, 韩敏. 多元混沌时间序列的因子回声状态网络预测模型. 自动化学报, 2015, 41(5): 1042-1046. doi: 10.16383/j.aas.2015.c140604
XU Mei-Ling, HAN Min. Factor Echo State Network for Multivariate Chaotic Time Series Prediction. ACTA AUTOMATICA SINICA, 2015, 41(5): 1042-1046. doi: 10.16383/j.aas.2015.c140604
Citation: XU Mei-Ling, HAN Min. Factor Echo State Network for Multivariate Chaotic Time Series Prediction. ACTA AUTOMATICA SINICA, 2015, 41(5): 1042-1046. doi: 10.16383/j.aas.2015.c140604

多元混沌时间序列的因子回声状态网络预测模型

doi: 10.16383/j.aas.2015.c140604
基金项目: 

国家自然科学基金(61374154), 国家重点基础 研究发展计划(973计划) (2013CB430403)资助

详细信息
    作者简介:

    许美玲 大连理工大学电子信息与电气工程学部博士研究生. 主要研究方向为神经网络和多元时间序列预测.E-mail: xuml@mail.dlut.edu.cn

    通讯作者:

    韩敏 大连理工大学电子信息与电气工程学部教授. 主要研究方向为神经网络, 模式识别和混沌时间序列预测. E-mail: minhan@dlut.edu.cn

Factor Echo State Network for Multivariate Chaotic Time Series Prediction

Funds: 

Supported by National Natural Science Foundation of China (61374154), and National Basic Research Program of China (973 Program) (2013CB430403)

  • 摘要: 针对采用回声状态网络预测多元混沌时间序列时存在的病态解问题, 本文建立了因子回声状态网络模型, 通过因子分析(Factor analysis, FA)方法提取高维储备池状态矩阵的公因子, 去除冗余和噪声成分. 利用降维后的因子变量与期望输出之间的线性回归关系, 求解网络未知参数. 基于Lorenz序列和大连月平均气温--降雨量的仿真实验验证了本文所提模型的有效性.
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出版历程
  • 收稿日期:  2014-08-19
  • 修回日期:  2014-12-04
  • 刊出日期:  2015-05-20

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