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基于小世界回声状态网的时间序列预测

伦淑娴 林健 姚显双

伦淑娴, 林健, 姚显双. 基于小世界回声状态网的时间序列预测. 自动化学报, 2015, 41(9): 1669-1679. doi: 10.16383/j.aas.2015.c150049
引用本文: 伦淑娴, 林健, 姚显双. 基于小世界回声状态网的时间序列预测. 自动化学报, 2015, 41(9): 1669-1679. doi: 10.16383/j.aas.2015.c150049
LUN Shu-Xian, LIN Jian, YAO Xian-Shuang. Time Series Prediction with an Improved Echo State Network Using Small World Network. ACTA AUTOMATICA SINICA, 2015, 41(9): 1669-1679. doi: 10.16383/j.aas.2015.c150049
Citation: LUN Shu-Xian, LIN Jian, YAO Xian-Shuang. Time Series Prediction with an Improved Echo State Network Using Small World Network. ACTA AUTOMATICA SINICA, 2015, 41(9): 1669-1679. doi: 10.16383/j.aas.2015.c150049

基于小世界回声状态网的时间序列预测

doi: 10.16383/j.aas.2015.c150049
基金项目: 

国家自然科学基金(61573072),辽宁省教育厅科技研究项目(L2015008),2011年辽宁省第一批次科学计划(2011402001),辽宁省自然科学基金(2014020143),辽宁省百千万人才工程(2012921061)资助

详细信息
    作者简介:

    林健 渤海大学工学院硕士研究生.主要研究方向为智能控制与滤波.E-mail:linjian19890821@163.com

    姚显双 渤海大学工学院硕士研究生.主要研究方向为智能控制与滤波.E-mail:yao8775336@163.com

    通讯作者:

    伦淑娴 渤海大学新能源学院教授.2005年获东北大学控制理论与控制工程专业博士学位.主要研究方向为智能控制与滤波,光伏发电系统建模与控制.本文通信作者.E-mail:jzlunzi@163.com

Time Series Prediction with an Improved Echo State Network Using Small World Network

Funds: 

Supported by National Nature Science Foundation of China (61573072), Science and Technology Research Projects of Department of Education of Liaoning Province (L2015008), the First Batch of Science and Technology Projects in Liaoning Province in 2011 (2011402001), Natural Science Foundation of Liaoning Province (2014020143), and Liaoning BaiQianWan Talents Program (2012921061)

  • 摘要: 为了提高时间序列的预测精度, 提出了利用改进的小世界网络优化泄露积分型回声状态网(Leaky-integrator echo state network, Leaky ESN)的时间序列预测方法. 首先提出一个改进型小世界网络, 其加边概率是节点间距离的负指数函数. 然后, 利用加边概率直接表示Leaky ESN储备池两个神经节点的连接权值, 取值范围为[0,1], 表征了节点间的连接程度. 利用这个新型小世界网络改进Leaky ESN的储备池神经节点的连接方式, 有目的地实现了稀疏连接, 减小了Leaky ESN储备池随机稀疏连接的盲目性, 提高了储备池的适应性.最后, 利用改进的Leaky ESN预测典型的非线性时间序列, 并利用Matlab仿真软件验证了本文提出方法的有效性. 与Leaky ESN相比, 本文提出的方法具有更高的预测精度和更短的训练时间.
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出版历程
  • 收稿日期:  2015-01-28
  • 修回日期:  2015-05-06
  • 刊出日期:  2015-09-20

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