引用本文:韩敏,姜涛,冯守渤.基于VMD循环随机跳跃状态网络的时间序列长期预测[J].控制与决策,2020,35(9):2175-2181
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基于VMD循环随机跳跃状态网络的时间序列长期预测
韩敏,姜涛,冯守渤
(大连理工大学电子信息与电气工程学部,辽宁大连116023)
摘要:
由于混沌系统的演化规律复杂,直接对混沌时间序列进行长期预测通常难以达到较好的效果.针对此问题,利用变分模态分解方法将混沌时间序列转化为一系列特征子序列,利用排列熵评估选取子序列个数的合理性,保证特征子序列包含了原序列长期演化趋势.此外,提出一种改进的确定性循环跳跃状态网络作为子序列的预测模型,该网络模型中的储备池采用单向环状连接和双向随机跳跃的拓扑结构,能够避免储备池确定连接结构造成的预测精度较低和随机连接造成网络的不稳定性问题.通过所提出模型对时间序列进行长期预测,采用多种评估手段对预测结果进行分析, 表明所提出模型对于长期预测具有较大的优势.
关键词:  混沌  时间序列预测  回声状态网络  变分模态分解  多尺度不变距离  预测
DOI:10.13195/j.kzyjc.2019.0060
分类号:TP183
基金项目:国家自然科学基金项目(61773087);中央高校科研基金项目(DUT18RC(6)005,DUT2018TB06).
Long-term prediction of time series based on VMD cyclic reservoir with random jumps network
HAN Min,JIANG Tao,FENG Shou-bo
(Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian116023,China)
Abstract:
Due to the complex evolution of chaotic systems, direct long-term prediction of chaotic time series is often difficult to achieve well performance. Considering this problem, the chaotic time series is transformed into a series of feature subsequences using the variational mode decomposition method. The rationality of selecting the number of subsequences is evaluated by the permutation entropy to ensure that the feature subsequences contain the long-term evolution trend of the original sequences. The modified cycle reservoir with regular jumps network is used as a predictive model for subsequences. The reservoir nodes of the modified model are connected in a uni-directional cycle with bi-directional shortcuts. This improvement avoids the problems of low prediction accuracy caused by deterministic connection structure and network instability caused by random connections in reservoir. Finally, this paper uses the proposed model to predict the time series in a long-term, and uses a variety of evaluation methods to analyze the prediction results, which show that the proposed model has a great advantage in long-term prediction.
Key words:  chaotic  time series prediction  echo state network  variational mode decomposition  multiscale complexity invariant distance  prediction

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